computational social science literature review

Computational Social Science: A Thematic Review

• Social Statistics

Research output : Preprint/Working paper › Working paper

Access to Document

• https://eprints.ncrm.ac.uk/id/eprint/4476

Fingerprint

• Research Community Computer Science 100%
• Social Research Computer Science 100%
• Data Analytics Computer Science 100%
• Field Research Computer Science 100%
• Impact Analysis Computer Science 100%
• Social Sciences Social Sciences 100%
• Impact Assessment Economics, Econometrics and Finance 100%
• Critical Review Psychology 100%

T1 - Computational Social Science: A Thematic Review

AU - Meckin, Robert

AU - Elliot, Mark

PY - 2021/10/19

Y1 - 2021/10/19

N2 - The explosion of social digital data and the concomitant increases in computational capabilities along the data analytics pipeline (data acquisition, storage and analysis) impact upon the possibilities and choices for conducting social research. This report examines the emerging research field called computational social science (CSS). The aim of this review is to offer insight into the shape of CSS, its questions and methodologies, and how these relate to and interact with different social science disciplines. Two searches and hand sorting identified 41 of the most highly cited publications. The papers were initially categorised into two main groups of papers: substantive-technical contributions and critical-review contributions. The groups were thematically analysed. As a validation and refinement exercise, a further search identified thirty of the most recent CSS papers, which were also categorised and analysed. The review focuses on the first 41 articles as well as several other relevant articles are discussed that were identified through citations, additional ad hoc searches, and personal conversations. The substantive-technical literature and critical-review literature can each be sub-divided into three groups, and findings from these six groups are described. In the discussion, we draw out points related to interdisciplinarity and potential implications of the findings for engagement research communities.

AB - The explosion of social digital data and the concomitant increases in computational capabilities along the data analytics pipeline (data acquisition, storage and analysis) impact upon the possibilities and choices for conducting social research. This report examines the emerging research field called computational social science (CSS). The aim of this review is to offer insight into the shape of CSS, its questions and methodologies, and how these relate to and interact with different social science disciplines. Two searches and hand sorting identified 41 of the most highly cited publications. The papers were initially categorised into two main groups of papers: substantive-technical contributions and critical-review contributions. The groups were thematically analysed. As a validation and refinement exercise, a further search identified thirty of the most recent CSS papers, which were also categorised and analysed. The review focuses on the first 41 articles as well as several other relevant articles are discussed that were identified through citations, additional ad hoc searches, and personal conversations. The substantive-technical literature and critical-review literature can each be sub-divided into three groups, and findings from these six groups are described. In the discussion, we draw out points related to interdisciplinarity and potential implications of the findings for engagement research communities.

M3 - Working paper

BT - Computational Social Science: A Thematic Review

PB - National Centre for Research Methods

• EPrints Home

Computational Social Science: A Thematic Review

Meckin, Robert and Elliot, Mark (2021) Computational Social Science: A Thematic Review. Other. National Centre for Research Methods.

The explosion of social digital data and the concomitant increases in computational capabilities along the data analytics pipeline (data acquisition, storage and analysis) impact upon the possibilities and choices for conducting social research. This report examines the emerging research field called computational social science (CSS). The aim of this review is to offer insight into the shape of CSS, its questions and methodologies, and how these relate to and interact with different social science disciplines. Two searches and hand sorting identified 41 of the most highly cited publications. The papers were initially categorised into two main groups of papers: substantive-technical contributions and critical-review contributions. The groups were thematically analysed. As a validation and refinement exercise, a further search identified thirty of the most recent CSS papers, which were also categorised and analysed. The review focuses on the first 41 articles as well as several other relevant articles are discussed that were identified through citations, additional ad hoc searches, and personal conversations. The substantive-technical literature and critical-review literature can each be sub-divided into three groups, and findings from these six groups are described. In the discussion, we draw out points related to interdisciplinarity and potential implications of the findings for engagement research communities.

Actions (login required)

• A-Z Publications

Annual Review of Psychology

Volume 75, 2024, review article, open access, computational social psychology.

• Fiery Cushman 1
• View Affiliations Hide Affiliations Affiliations: Department of Psychology, Harvard University, Cambridge, Massachusetts, USA; email:  [email protected]
• Vol. 75:625-652 (Volume publication date January 2024) https://doi.org/10.1146/annurev-psych-021323-040420
• First published as a Review in Advance on August 04, 2023
• Copyright © 2024 by the author(s). This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Article metrics loading...

Full text loading...

Literature Cited

• Aka A , Bhatia S. 2021 . What I like is what I remember: memory modulation and preferential choice. J. Exp. Psychol. Gen. 150 : 10 2175– 84 [Google Scholar]
• Alicke MD. 2000 . Culpable control and the psychology of blame. Psychol. Bull. 126 : 4 556– 74 [Google Scholar]
• André JB , Baumard N. 2011 . The evolution of fairness in a biological market. Evolution 65 : 5 1447– 56 [Google Scholar]
• Andreoni J. 1990 . Impure altruism and donations to public goods: a theory of warm-glow giving. Econ. J. 100 : 401 464– 77 [Google Scholar]
• Asch SE. 1955 . Opinions and social pressure. Sci. Am. 193 : 5 31– 35 [Google Scholar]
• Axelrod R , Hamilton WD. 1981 . The evolution of cooperation. Science 211 : 4489 1390– 96 [Google Scholar]
• Baker CL , Jara-Ettinger J , Saxe R , Tenenbaum JB 2017 . Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nat. Hum. Behav. 1 : 0064 [Google Scholar]
• Baker CL , Saxe R , Tenenbaum JB. 2009 . Action understanding as inverse planning. Cognition 113 : 3 329– 49 [Google Scholar]
• Barclay P , Willer R. 2007 . Partner choice creates competitive altruism in humans. Proc. R. Soc. B 274 : 1610 . 749– 53 [Google Scholar]
• Bass FM. 1969 . A new product growth for model consumer durables. Manag. Sci. 15 : 5 215– 27 [Google Scholar]
• Battigalli P , Dufwenberg M. 2009 . Dynamic psychological games. J. Econ. Theory 144 : 1 1– 35 [Google Scholar]
• Baumard N , André JB , Sperber D. 2013 . A mutualistic approach to morality: the evolution of fairness by partner choice. Behav. Brain Sci. 36 : 1 59– 78 [Google Scholar]
• Bear A , Rand DG. 2016 . Intuition, deliberation, and the evolution of cooperation. PNAS 113 : 4 936– 41 [Google Scholar]
• Behrens TE , Hunt LT , Woolrich MW , Rushworth MF. 2008 . Associative learning of social value. Nature 456 : 7219 245– 49 [Google Scholar]
• Bem DJ. 1972 . Self-perception theory. Advances in Experimental Social Psychology , Vol. 6 L Berkowitz 1– 62 . Amsterdam: Elsevier [Google Scholar]
• Bernhard RM , Cushman F. 2022 . Extortion, intuition, and the dark side of reciprocity. Cognition 228 : 105215 [Google Scholar]
• Bernhard RM , LeBaron H , Phillips J. 2022 . It's not what you did, it's what you could have done. Cognition 228 : 105222 [Google Scholar]
• Bhui R , Lai L , Gershman SJ. 2021 . Resource-rational decision making. Curr. Opin. Behav. Sci. 41 : 15– 21 [Google Scholar]
• Bicchieri C. 1990 . Norms of cooperation. Ethics 100 : 4 838– 61 [Google Scholar]
• Binmore K. 2005 . Natural Justice Oxford, UK: Oxford Univ. Press [Google Scholar]
• Boyd R , Richerson PJ. 1988 . Culture and the Evolutionary Process Chicago: Univ. Chicago Press [Google Scholar]
• Boyd R , Richerson PJ. 1992 . Punishment allows the evolution of cooperation (or anything else) in sizable groups. Ethol. Sociobiol. 13 : 3 171– 95 [Google Scholar]
• Boyd R , Richerson PJ , Henrich J. 2011 . The cultural niche: why social learning is essential for human adaptation. PNAS 108 : Suppl 2 10918– 25 [Google Scholar]
• Brehm JW. 1956 . Postdecision changes in the desirability of alternatives. J. Abnormal Soc. Psychol. 52 : 3 384– 89 [Google Scholar]
• Callaway F , van Opheusden B , Gul S , Das P , Krueger PM et al. 2022 . Rational use of cognitive resources in human planning. Nat. Hum. Behav. 6 : 8 1112– 25 [Google Scholar]
• Cao J , Kleiman-Weiner M , Banaji MR. 2017 . Statistically inaccurate and morally unfair judgements via base rate intrusion. Nat. Hum. Behav. 1 : 10 738– 42 [Google Scholar]
• Cao J , Kleiman-Weiner M , Banaji MR. 2019 . People make the same Bayesian judgment they criticize in others. Psychol. Sci. 30 : 1 20– 31 [Google Scholar]
• Cao Y , Enke B , Falk A , Giuliano P , Nunn N. 2021 . Herding, warfare, and a culture of honor: global evidence NBER Work. Pap 29250 [Google Scholar]
• Capraro V , Rand DG. 2018 . Do the right thing: experimental evidence that preferences for moral behavior, rather than equity or efficiency per se, drive human prosociality. Judgm. Decis. Mak. 13 : 1 99– 111 [Google Scholar]
• Carlsmith KM , Darley JM , Robinson PH. 2002 . Why do we punish? Deterrence and just deserts as motives for punishment. J. Pers. Soc. Psychol. 83 : 2 284– 99 [Google Scholar]
• Chang LJ , Smith A. 2015 . Social emotions and psychological games. Curr. Opin. Behav. Sci. 5 : 133– 40 [Google Scholar]
• Chang LJ , Smith A , Dufwenberg M , Sanfey AG. 2011 . Triangulating the neural, psychological, and economic bases of guilt aversion. Neuron 70 : 3 560– 72 [Google Scholar]
• Charpentier CJ , Iigaya K , O'Doherty JP 2020 . A neuro-computational account of arbitration between choice imitation and goal emulation during human observational learning. Neuron 106 : 4 687– 99 [Google Scholar]
• Cikara M , Botvinick MM , Fiske ST. 2011 . Us versus them: Social identity shapes neural responses to intergroup competition and harm. Psychol. Sci. 22 : 3 306– 13 [Google Scholar]
• Cockburn J , Collins AG , Frank MJ. 2014 . A reinforcement learning mechanism responsible for the valuation of free choice. Neuron 83 : 3 551– 57 [Google Scholar]
• Coleman JS , James J. 1961 . The equilibrium size distribution of freely-forming groups. Sociometry 24 : 1 36– 45 [Google Scholar]
• Critcher CR , Inbar Y , Pizarro DA. 2013 . How quick decisions illuminate moral character. Soc. Psychol. Pers. Sci. 4 : 3 308– 15 [Google Scholar]
• Crockett MJ. 2013 . Models of morality. Trends Cogn. Sci. 17 : 8 363– 66 [Google Scholar]
• Crockett MJ , Everett JA , Gill M , Siegel JZ. 2021 . The relational logic of moral inference. Adv. Exp. Soc. Psychol. 64 : 1– 64 [Google Scholar]
• Crumpler H , Grossman PJ. 2008 . An experimental test of warm glow giving. J. Public Econ. 92 : 5–6 1011– 21 [Google Scholar]
• Cushman F. 2008 . Crime and punishment: distinguishing the roles of causal and intentional analyses in moral judgment. Cognition 108 : 2 353– 80 [Google Scholar]
• Cushman F. 2013 . Action, outcome, and value: a dual-system framework for morality. Pers. Soc. Psychol. Rev. 17 : 3 273– 92 [Google Scholar]
• Cushman F. 2020 . Rationalization is rational. Behav. Brain Sci. 43 : e28 [Google Scholar]
• Cushman F , Macindoe O. 2009 . The coevolution of punishment and prosociality among learning agents. Proceedings of the Annual Meeting of the Cognitive Science Society , Vol. 31 1174– 79 . Seattle, WA: Cogn. Sci. Soc. [Google Scholar]
• Cushman F , Morris A. 2015 . Habitual control of goal selection in humans. PNAS 112 : 45 13817– 22 [Google Scholar]
• Diaconescu AO , Mathys C , Weber LA , Daunizeau J , Kasper L et al. 2014 . Inferring on the intentions of others by hierarchical Bayesian learning. PLOS Comput. Biol. 10 : 9 e1003810 [Google Scholar]
• Diaconescu AO , Mathys C , Weber LA , Kasper L , Mauer J , Stephan KE. 2017 . Hierarchical prediction errors in midbrain and septum during social learning. Soc. Cogn. Affect. Neurosci. 12 : 4 618– 34 [Google Scholar]
• Fehr E , Fischbacher U. 2004 . Third-party punishment and social norms. Evol. Hum. Behav. 25 : 2 63– 87 [Google Scholar]
• Fehr E , Gächter S. 2002 . Altruistic punishment in humans. Nature 415 : 6868 137– 40 [Google Scholar]
• Fehr E , Schmidt KM. 1999 . A theory of fairness, competition, and cooperation. Q. J. Econom. 114 : 3 817– 68 [Google Scholar]
• FeldmanHall O , Shenhav A. 2019 . Resolving uncertainty in a social world. Nat. Hum. Behav. 3 : 5 426– 35 [Google Scholar]
• Frank RH. 1988 . Passions Within Reason: The Strategic Role of the Emotions New York: W.W. Norton & Co. [Google Scholar]
• Fudenberg D , Newey W , Strack P , Strzalecki T. 2020 . Testing the drift-diffusion model. PNAS 117 : 52 33141– 48 [Google Scholar]
• Gates V , Callaway F , Ho MK , Griffiths TL. 2021 . A rational model of people's inferences about others' preferences based on response times. Cognition 217 : 104885 [Google Scholar]
• Geanakoplos J , Pearce D , Stacchetti E. 1989 . Psychological games and sequential rationality. Games Econ. Behav. 1 : 1 60– 79 [Google Scholar]
• Gershman SJ 2023 . The rational analysis of memory. In Oxford Handbook of Human Memory ed. M Kahana, A Wagner Oxford, UK: Oxford Univ. Press In press [Google Scholar]
• Gershman SJ , Pouncy HT , Gweon H. 2017 . Learning the structure of social influence. Cogn. Sci. 41 : 545– 75 [Google Scholar]
• Gerstenberg T , Goodman ND , Lagnado DA , Tenenbaum JB. 2021 . A counterfactual simulation model of causal judgments for physical events. Psychol. Rev. 128 : 5 936– 75 [Google Scholar]
• Gintis H , Smith EA , Bowles S. 2001 . Costly signaling and cooperation. J. Theor. Biol. 213 : 1 103– 19 [Google Scholar]
• Gold JI , Shadlen MN. 2007 . The neural basis of decision making. Annu. Rev. Neurosci. 30 : 535– 74 [Google Scholar]
• Goodman ND , Frank MC. 2016 . Pragmatic language interpretation as probabilistic inference. Trends Cogn. Sci. 20 : 11 818– 29 [Google Scholar]
• Grafen A. 1990 . Biological signals as handicaps. J. Theor. Biol. 144 : 4 517– 46 [Google Scholar]
• Greene JD. 2008 . The secret joke of Kant's soul. Moral Psychol . 3 : 35– 79 [Google Scholar]
• Griffiths TL , Kemp C , Tenenbaum JB. 2008 . Bayesian models of cognition. The Cambridge Handbook of Computational Psychology R Sun 59– 100 . Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Gweon H. 2021 . Inferential social learning: cognitive foundations of human social learning and teaching. Trends Cogn. Sci. 25 : 10 896– 910 [Google Scholar]
• Hackel LM , Doll BB , Amodio DM. 2015 . Instrumental learning of traits versus rewards: dissociable neural correlates and effects on choice. Nat. Neurosci. 18 : 9 1233– 35 [Google Scholar]
• Halpern JY , Hitchcock C. 2015 . Graded causation and defaults. Br. J. Philos. Sci. 66 : 2 413– 57 [Google Scholar]
• Halpern JY , Kleiman-Weiner M. 2018 . Towards formal definitions of blameworthiness, intention, and moral responsibility. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence , Vol. 32 1853– 60 . New Orleans, LA: AAAI [Google Scholar]
• Harris A , Hutcherson CA. 2022 . Temporal dynamics of decision making: a synthesis of computational and neurophysiological approaches. WIREs Cogn. Sci. 13 : 3 e1586 [Google Scholar]
• Harsanyi JC , Selten R. 1988 . A General Theory of Equilibrium Selection in Games . Cambridge, MA: MIT Press [Google Scholar]
• Heider F. 1958 . The Psychology of Interpersonal Relations New York: John Wiley & Sons [Google Scholar]
• Heider F , Simmel M. 1944 . An experimental study of apparent behavior. Am. J. Psychol. 57 : 2 243– 59 [Google Scholar]
• Henrich J. 2004 . Cultural group selection, coevolutionary processes and large-scale cooperation. J. Econ. Behav. Organ. 53 : 1 3– 35 [Google Scholar]
• Henrich J. 2009 . The evolution of costly displays, cooperation and religion: credibility enhancing displays and their implications for cultural evolution. Evol. Hum. Behav. 30 : 4 244– 60 [Google Scholar]
• Herrmann B , Thoni C , Gachter S. 2008 . Antisocial punishment across societies. Science 319 : 5868 1362– 67 [Google Scholar]
• Hitchcock C , Knobe J. 2009 . Cause and norm. J. Philos. 106 : 11 587– 612 [Google Scholar]
• Ho MK , Abel D , Correa CG , Littman ML , Cohen JD , Griffiths TL. 2022a . People construct simplified mental representations to plan. Nature 606 : 7912 129– 36 [Google Scholar]
• Ho MK , Cushman F , Littman ML , Austerweil JL. 2019 . People teach with rewards and punishments as communication, not reinforcements. J. Exp. Psychol. Gen. 148 : 3 520– 49 [Google Scholar]
• Ho MK , Cushman F , Littman ML , Austerweil JL. 2021 . Communication in action: planning and interpreting communicative demonstrations. J. Exp. Psychol. Gen. 150 : 11 2246– 72 [Google Scholar]
• Ho MK , MacGlashan J , Littman ML , Cushman F. 2017 . Social is special: a normative framework for teaching with and learning from evaluative feedback. Cognition 167 : 91– 106 [Google Scholar]
• Ho MK , Saxe R , Cushman F. 2022b . Planning with theory of mind. Trends Cogn. Sci. 26 : 11 959– 71 [Google Scholar]
• Hoffman M , Yoeli E , Nowak MA. 2015 . Cooperate without looking: why we care what people think and not just what they do. PNAS 112 : 6 1727– 32 [Google Scholar]
• Holyoak KJ , Simon D. 1999 . Bidirectional reasoning in decision making by constraint satisfaction. J. Exp. Psychol. Gen. 128 : 1 3– 31 [Google Scholar]
• Hornsby AN , Love BC. 2020 . How decisions and the desire for coherency shape subjective preferences over time. Cognition 200 : 104244 [Google Scholar]
• Hsu M , Anen C , Quartz SR. 2008 . The right and the good: distributive justice and neural encoding of equity and efficiency. Science 320 : 5879 1092– 95 [Google Scholar]
• Hutcherson CA , Bushong B , Rangel A. 2015 . A neurocomputational model of altruistic choice and its implications. Neuron 87 : 2 451– 62 [Google Scholar]
• Icard TF , Kominsky JF , Knobe J. 2017 . Normality and actual causal strength. Cognition 161 : 80– 93 [Google Scholar]
• Izuma K , Adolphs R. 2013 . Social manipulation of preference in the human brain. Neuron 78 : 3 563– 73 [Google Scholar]
• Izuma K , Saito DN , Sadato N. 2008 . Processing of social and monetary rewards in the human striatum. Neuron 58 : 2 284– 94 [Google Scholar]
• Izuma K , Saito DN , Sadato N. 2010 . Processing of the incentive for social approval in the ventral striatum during charitable donation. J. Cogn. Neurosci. 22 : 4 621– 31 [Google Scholar]
• Jara-Ettinger J. 2019 . Theory of mind as inverse reinforcement learning. Curr. Opin. Behav. Sci. 29 : 105– 10 [Google Scholar]
• Jara-Ettinger J , Gweon H , Schulz LE , Tenenbaum JB. 2016 . The naïve utility calculus: computational principles underlying commonsense psychology. Trends Cogn. Sci. 20 : 8 589– 604 [Google Scholar]
• Jara-Ettinger J , Tenenbaum JB , Schulz LE. 2015 . Not so innocent: toddlers' inferences about costs and culpability. Psychol. Sci. 26 : 5 633– 40 [Google Scholar]
• Jordan JJ , Hoffman M , Bloom P , Rand DG. 2016a . Third-party punishment as a costly signal of trustworthiness. Nature 530 : 7591 473– 76 [Google Scholar]
• Jordan JJ , Hoffman M , Nowak MA , Rand DG. 2016b . Uncalculating cooperation is used to signal trustworthiness. PNAS 113 : 31 8658– 63 [Google Scholar]
• Kahneman D. 2011 . Thinking, Fast and Slow . London: Macmillan [Google Scholar]
• Kalkstein DA , Hook CJ , Hard BM , Walton GM. 2023 . Social norms govern what behaviors come to mind—and what do not. J. Pers. Soc. Psychol. 124 : 6 1203– 29 [Google Scholar]
• Kelley HH. 1973 . The processes of causal attribution. Am. Psychol. 28 : 2 107– 28 [Google Scholar]
• Keramati M , Smittenaar P , Dolan RJ , Dayan P. 2016 . Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum. PNAS 113 : 45 12868– 73 [Google Scholar]
• Kim M , Park B , Young L. 2020 . The psychology of motivated versus rational impression updating. Trends Cogn. Sci. 24 : 2 101– 11 [Google Scholar]
• Kleiman-Weiner M , Gerstenberg T , Levine S , Tenenbaum JB. 2015 . Inference of intention and permissibility in moral decision making. Proceedings of the 37th Annual Conference of the Cognitive Science Society 1123– 28 . Seattle, WA: Cogn. Sci. Soc. [Google Scholar]
• Kleiman-Weiner M , Ho MK , Austerweil JL , Littman ML , Tenenbaum JB. 2016 . Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction. Proceedings of the 38th Annual Conference of the Cognitive Science Society 1679– 84 . Seattle, WA: Cogn. Sci. Soc. [Google Scholar]
• Kleiman-Weiner M , Saxe R , Tenenbaum JB 2017 . Learning a commonsense moral theory. Cognition 167 : 107– 23 [Google Scholar]
• Knobe J , Nichols S. 2011 . Free will and the bounds of the self. The Oxford Handbook of Free Will R Kane 530– 54 . Oxford, UK: Oxford Univ. Press. , 2nd ed.. [Google Scholar]
• Koster R , Duzel E , Dolan RJ. 2015 . Action and valence modulate choice and choice-induced preference change. PLOS ONE 10 : 3 e0119682 [Google Scholar]
• Kraft-Todd G , Kleiman-Weiner M , Young L. 2022 . Virtue discounting: Observability reduces moral actors' perceived virtue. PsyArXiv, Febr. 27. https://doi.org/10.31234/osf.io/hsjta
• Krajbich I , Armel C , Rangel A. 2010 . Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13 : 10 1292– 98 [Google Scholar]
• Kunda Z. 1990 . The case for motivated reasoning. Psychol. Bull. 108 : 3 480– 98 [Google Scholar]
• Lau T , Pouncy HT , Gershman SJ , Cikara M. 2018 . Discovering social groups via latent structure learning. J. Exp. Psychol. Gen. 147 : 12 1881– 91 [Google Scholar]
• Le Pargneux A , Chater N , Zeitoun H 2023 . Contractualist reasoning influences moral judgment and decision making. PsyArXiv, May 4. https://doi.org/10.31234/osf.io/p4cyx
• Leong YC , Hughes BL , Wang Y , Zaki J. 2019 . Neurocomputational mechanisms underlying motivated seeing. Nat. Hum. Behav. 3 : 9 962– 73 [Google Scholar]
• Levine S , Kleiman-Weiner M , Schulz L , Tenenbaum J , Cushman F. 2020 . The logic of universalization guides moral judgment. PNAS 117 : 42 26158– 69 [Google Scholar]
• Lewin K. 1936 . Principles of Topological Psychology New York: McGraw-Hill [Google Scholar]
• Lewis D. 2013 . Counterfactuals New York: John Wiley & Sons [Google Scholar]
• Lieder F , Griffiths TL. 2020 . Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 43 : e1 [Google Scholar]
• Littman ML. 2001 . Value-function reinforcement learning in Markov games. Cogn. Syst. Res. 2 : 1 55– 66 [Google Scholar]
• Liu S , Ullman TD , Tenenbaum JB , Spelke ES. 2017 . Ten-month-old infants infer the value of goals from the costs of actions. Science 358 : 6366 1038– 41 [Google Scholar]
• Lockwood PL , Klein-Flügge MC , Abdurahman A , Crockett MJ. 2020 . Model-free decision making is prioritized when learning to avoid harming others. PNAS 117 : 44 27719– 30 [Google Scholar]
• Lombrozo T. 2010 . Causal-explanatory pluralism: how intentions, functions, and mechanisms influence causal ascriptions. Cogn. Psychol. 61 : 4 303– 32 [Google Scholar]
• Maier M , Cheung V , Bartoš F , Lieder F. 2023 . Learning from consequences shapes reliance on moral rules versus cost-benefit reasoning. PsyArXiv, Apr. 21. https://doi.org/10.31234/osf.io/gjf3h
• Malle BF , Guglielmo S , Monroe AE. 2014 . A theory of blame. Psychol. Inq. 25 : 2 147– 86 [Google Scholar]
• Marr D. 1982 . Vision: A Computational Investigation into the Human Representation and Processing of Visual Information Cambridge, MA: MIT Press [Google Scholar]
• Martin JW , Cushman F. 2016 . Why we forgive what can't be controlled. Cognition 147 : 133– 43 [Google Scholar]
• Michotte A. 2017 . The Perception of Causality London: Routledge [Google Scholar]
• Mikhail J. 2007 . Universal moral grammar: theory, evidence and the future. Trends Cogn. Sci. 11 : 4 143– 52 [Google Scholar]
• Milgram S , Bickman L , Berkowitz L. 1969 . Note on the drawing power of crowds of different size. J. Pers. Soc. Psychol. 13 : 2 79– 82 [Google Scholar]
• Morris A , Cushman F. 2018 . A common framework for theories of norm compliance. Soc. Philos. Policy 35 : 1 101– 27 [Google Scholar]
• Morris A , MacGlashan J , Littman ML , Cushman F. 2017 . Evolution of flexibility and rigidity in retaliatory punishment. PNAS 114 : 39 10396– 401 [Google Scholar]
• Morris A , Phillips JS , Huang K , Cushman F. 2021 . Generating options and choosing between them depend on distinct forms of value representation. Psychol. Sci. 32 : 11 1731– 46 [Google Scholar]
• Morris A , Phillips JS , Icard T , Knobe J , Gerstenberg T , Cushman F. 2018 . Causal judgments approximate the effectiveness of future interventions. PsyArXiv, Apr. 26. https://doi.org/10.31234/osf.io/nq53z
• Najar A , Bonnet E , Bahrami B , Palminteri S. 2020 . The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning. PLOS Biol . 18 : 12 e3001028 [Google Scholar]
• Nakayama K , Shimojo S 1992 . Experiencing and perceiving visual surfaces. Science 257 : 1357– 63 [Google Scholar]
• Nash J. 1951 . Non-cooperative games. Ann. Math. 54 : 2 286– 95 [Google Scholar]
• Nash JF. 1950 . The bargaining problem. Econometrica 18 : 2 155– 62 [Google Scholar]
• Newell A , Simon H. 1956 . The logic theory machine—a complex information processing system. IRE Trans. Inform. Theory 2 : 3 61– 79 [Google Scholar]
• Nichols S. 2021 . Rational Rules: Towards a Theory of Moral Learning Oxford, UK: Oxford University Press [Google Scholar]
• Nisbett RE. 2018 . Culture of Honor: The Psychology of Violence in the South London: Routledge [Google Scholar]
• Nowak MA. 2006 . Five rules for the evolution of cooperation. Science 314 : 5805 1560– 63 [Google Scholar]
• O'Connor C. 2019 . The Origins of Unfairness: Social Categories and Cultural Evolution Oxford, UK: Oxford Univ. Press [Google Scholar]
• Olsson A , Phelps EA. 2007 . Social learning of fear. Nat. Neurosci. 10 : 9 1095– 102 [Google Scholar]
• Ong DC , Zaki J , Goodman ND. 2019 . Computational models of emotion inference in theory of mind: a review and roadmap. Top. Cogn. Sci. 11 : 2 338– 57 [Google Scholar]
• Ostrom E. 2000 . Collective action and the evolution of social norms. J. Econ. Perspect. 14 : 3 137– 58 [Google Scholar]
• Park B , Kim M , Young L. 2021 . An examination of accurate versus “biased” mentalizing in moral and economic decision-making. The Neural Basis of Mentalizing M Gilead, KN Ochsner 537– 54 . Cham, Switz.: Springer [Google Scholar]
• Patil I , Zucchelli MM , Kool W , Campbell S , Fornasier F et al. 2021 . Reasoning supports utilitarian resolutions to moral dilemmas across diverse measures. J. Pers. Soc. Psychol. 120 : 2 443– 60 [Google Scholar]
• Pearl J. 2009 . Causality Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Phan KL , Sripada CS , Angstadt M , McCabe K. 2010 . Reputation for reciprocity engages the brain reward center. PNAS 107 : 29 13099– 104 [Google Scholar]
• Phillips J , Cushman F. 2017 . Morality constrains the default representation of what is possible. PNAS 114 : 18 4649– 54 [Google Scholar]
• Phillips J , Morris A , Cushman F. 2019 . How we know what not to think. Trends Cogn. Sci. 23 : 12 1026– 40 [Google Scholar]
• Pinker S. 2010 . The cognitive niche: coevolution of intelligence, sociality, and language. PNAS 107 : Suppl. 2 8993– 99 [Google Scholar]
• Pizarro D , Uhlmann E , Salovey P. 2003 . Asymmetry in judgments of moral blame and praise: the role of perceived metadesires. Psychol. Sci. 14 : 3 267– 72 [Google Scholar]
• Quillien T , German TC. 2021 . A simple definition of “intentionally. .” Cognition 214 : 104806 [Google Scholar]
• Quillien T , Lucas CG. 2022 . Counterfactuals and the logic of causal selection. PsyArXiv, June 13. https://doi.org/10.31234/osf.io/ts76y [Crossref]
• Rabin M. 1993 . Incorporating fairness into game theory and economics. Am. Econ. Rev. 83 : 5 1281– 302 [Google Scholar]
• Rabinowitz N , Perbet F , Song F , Zhang C , Eslami SA , Botvinick M. 2018 . Machine theory of mind. PMLR 80 : 4218– 27 [Google Scholar]
• Rand DG , Greene JD , Nowak MA. 2012 . Spontaneous giving and calculated greed. Nature 489 : 7416 427– 30 [Google Scholar]
• Rand DG , Peysakhovich A , Kraft-Todd GT , Newman GE , Wurzbacher O et al. 2014 . Social heuristics shape intuitive cooperation. Nat. Commun. 5 : 1 3677 [Google Scholar]
• Rangel A , Camerer C , Montague PR. 2008 . A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9 : 7 545– 56 [Google Scholar]
• Ratcliff R , Smith PL. 2004 . A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111 : 2 333– 67 [Google Scholar]
• Redish AD. 2022 . Changing How We Choose: The New Science of Morality Cambridge, MA: MIT Press [Google Scholar]
• Rescorla RA. 1972 . A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. Classical Conditioning II: Current Research and Theory AH Black, WF Prokasy 64– 99 . New York: Appleton-Century-Crofts [Google Scholar]
• Rilling JK , Sanfey AG. 2011 . The neuroscience of social decision-making. Annu. Rev. Psychol. 62 : 23– 48 [Google Scholar]
• Roberts G. 1998 . Competitive altruism: from reciprocity to the handicap principle. Proc. R. Soc. B 265 : 1394 . 427– 31 [Google Scholar]
• Ross L. 1977 . The intuitive psychologist and his shortcomings: distortions in the attribution process. Adv. Exp. Soc. Psychol. 10 : 173– 220 [Google Scholar]
• Ruff CC , Fehr E. 2014 . The neurobiology of rewards and values in social decision making. Nat. Rev. Neurosci. 15 : 8 549– 62 [Google Scholar]
• Ruiz-Serra J , Harré MS. 2023 . Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems. Algorithms 16 : 2 68 [Google Scholar]
• Sanfey AG , Rilling JK , Aronson JA , Nystrom LE , Cohen JD. 2003 . The neural basis of economic decision-making in the ultimatum game. Science 300 : 5626 1755– 58 [Google Scholar]
• Saxe R , Houlihan SD. 2017 . Formalizing emotion concepts within a Bayesian model of theory of mind. Curr. Opin. Psychol. 17 : 15– 21 [Google Scholar]
• Schelling TC. 1960 . The Strategy of Conflict Cambridge, MA: Harvard Univ. Press [Google Scholar]
• Schelling TC. 1971 . Dynamic models of segregation. J. Math. Sociol. 1 : 2 143– 86 [Google Scholar]
• Sell AN. 2011 . The recalibrational theory and violent anger. Aggress. Violent Behav. 16 : 5 381– 89 [Google Scholar]
• Sell AN , Sznycer D , Al-Shawaf L , Lim J , Krauss A et al. 2017 . The grammar of anger: mapping the computational architecture of a recalibrational emotion. Cognition 168 : 110– 28 [Google Scholar]
• Shafto P , Goodman ND , Griffiths TL. 2014 . A rational account of pedagogical reasoning: teaching by, and learning from, examples. Cogn. Psychol. 71 : 55– 89 [Google Scholar]
• Sharot T , De Martino B , Dolan RJ. 2009 . How choice reveals and shapes expected hedonic outcome. J. Neurosci. 29 : 12 3760– 65 [Google Scholar]
• Shin YS , Niv Y. 2021 . Biased evaluations emerge from inferring hidden causes. Nat. Hum. Behav. 5 : 9 1180– 89 [Google Scholar]
• Shultz TR , Lepper MR 1999 . Computer simulation of cognitive dissonance reduction. Cognitive Dissonance: Progress on a Pivotal Theory in Social Psychology E Harmon-Jones, J Mills 235– 65 . Washington, DC: APA [Google Scholar]
• Shum M , Kleiman-Weiner M , Littman ML , Tenenbaum JB. 2019 . Theory of minds: understanding behavior in groups through inverse planning. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence , Vol. 33 6163– 70 . New Orleans, LA: AAAI [Google Scholar]
• Siegel JZ , Mathys C , Rutledge RB , Crockett MJ. 2018 . Beliefs about bad people are volatile. Nat. Hum. Behav. 2 : 10 750– 56 [Google Scholar]
• Silver D , Schrittwieser J , Simonyan K , Antonoglou I , Huang A et al. 2017 . Mastering the game of Go without human knowledge. Nature 550 : 7676 354– 59 [Google Scholar]
• Simon HA. 1956 . Rational choice and the structure of the environment. Psychol. Rev. 63 : 2 129– 38 [Google Scholar]
• Sloman SA. 1996 . The empirical case for two systems of reasoning. Psychol. Bull. 119 : 1 3– 22 [Google Scholar]
• Smith JM. 1991 . Honest signalling: the Philip Sidney game. Anim. Behav. 42 : 6 1034– 35 [Google Scholar]
• Smith JM. 1994 . Must reliable signals always be costly?. Anim. Behav. 47 : 5 1115– 20 [Google Scholar]
• Smith JM , Price GR. 1973 . The logic of animal conflict. Nature 246 : 5427 15– 18 [Google Scholar]
• Son JY , Bhandari A , FeldmanHall O. 2019 . Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions. Sci. Rep. 9 : 11625 [Google Scholar]
• Sosa FA , Ullman T , Tenenbaum JB , Gershman SJ , Gerstenberg T. 2021 . Moral dynamics: grounding moral judgment in intuitive physics and intuitive psychology. Cognition 217 : 104890 [Google Scholar]
• Sullivan N , Hutcherson C , Harris A , Rangel A. 2015 . Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychol. Sci. 26 : 2 122– 34 [Google Scholar]
• Sutton RS. 1988 . Learning to predict by the methods of temporal differences. Mach. Learn. 3 : 9– 44 [Google Scholar]
• Sutton RS , Barto AG. 2018 . Reinforcement Learning: An Introduction Cambridge, MA: MIT Press [Google Scholar]
• Swidler A. 1986 . Culture in action: symbols and strategies. Am. Sociol. Rev. 51 : 2 273– 86 [Google Scholar]
• Sznycer D. 2019 . Forms and functions of the self-conscious emotions. Trends Cogn. Sci. 23 : 2 143– 57 [Google Scholar]
• Sznycer D , Tooby J , Cosmides L , Porat R , Shalvi S , Halperin E. 2016 . Shame closely tracks the threat of devaluation by others, even across cultures. PNAS 113 : 10 2625– 30 [Google Scholar]
• Tamir DI , Thornton MA. 2018 . Modeling the predictive social mind. Trends Cogn. Sci. 22 : 3 201– 12 [Google Scholar]
• Tappin BM , Pennycook G , Rand DG. 2020 . Thinking clearly about causal inferences of politically motivated reasoning: why paradigmatic study designs often undermine causal inference. Curr. Opin. Behav. Sci. 34 : 81– 87 [Google Scholar]
• Tenenbaum JB , Griffiths TL , Kemp C 2006 . Theory-based Bayesian models of inductive learning and reasoning. Trends Cogn. Sci 10 : 309– 18 [Google Scholar]
• Thompson B , Van Opheusden B , Sumers T , Griffiths T. 2022 . Complex cognitive algorithms preserved by selective social learning in experimental populations. Science 376 : 6588 95– 98 [Google Scholar]
• Tinbergen N. 1963 . On aims and methods of ethology. Z. Tierpsychol. 20 : 4 410– 33 [Google Scholar]
• Todd PM , Gigerenzer G. 2000 . Précis of simple heuristics that make us smart. Behav. Brain Sci. 23 : 5 727– 41 [Google Scholar]
• Tomasello M. 2009 . The Cultural Origins of Human Cognition Cambridge, MA: Harvard Univ. Press [Google Scholar]
• Treadway MT , Buckholtz JW , Martin JW , Jan K , Asplund CL et al. 2014 . Corticolimbic gating of emotion-driven punishment. Nat. Neurosci. 17 : 9 1270– 75 [Google Scholar]
• Trivers RL. 1971 . The evolution of reciprocal altruism. Q. Rev. Biol. 46 : 1 35– 57 [Google Scholar]
• Uhlmann EL , Pizarro DA , Diermeier D. 2015 . A person-centered approach to moral judgment. Perspect. Psychol. Sci. 10 : 1 72– 81 [Google Scholar]
• van Baar JM , Chang LJ , Sanfey AG. 2019 . The computational and neural substrates of moral strategies in social decision-making. Nat. Commun. 10 : 1 1483 [Google Scholar]
• Van den Bos W , van Dijk E , Westenberg M , Rombouts SA , Crone EA. 2009 . What motivates repayment? Neural correlates of reciprocity in the trust game. Soc. Cogn. Affect. Neurosci. 4 : 3 294– 304 [Google Scholar]
• Vasilyeva N , Blanchard T , Lombrozo T. 2018 . Stable causal relationships are better causal relationships. Cogn. Sci. 42 : 4 1265– 96 [Google Scholar]
• Veblen T. 1889 . The Theory of the Leisure Class London: George Allen & Unwin [Google Scholar]
• Vélez N , Chen A , Burke T , Cushman F , Gershman S. 2023 . Teachers recruit mentalizing regions to represent learners' beliefs. PNAS 120 : 22 e2215015120 [Google Scholar]
• Vélez N , Gweon H. 2019 . Integrating incomplete information with imperfect advice. Top. Cogn. Sci. 11 : 2 299– 315 [Google Scholar]
• Vélez N , Gweon H. 2021 . Learning from other minds: an optimistic critique of reinforcement learning models of social learning. Curr. Opin. Behav. Sci. 38 : 110– 15 [Google Scholar]
• Vinckier F , Rigoux L , Kurniawan IT , Hu C , Bourgeois-Gironde S et al. 2019 . Sour grapes and sweet victories: how actions shape preferences. PLOS Comput. Biol. 15 : 1 e1006499 [Google Scholar]
• Von Hippel W , Trivers R. 2011 . The evolution and psychology of self-deception. Behav. Brain Sci. 34 : 1 1– 56 [Google Scholar]
• Vul E , Pashler H. 2008 . Measuring the crowd within: probabilistic representations within individuals. Psychol. Sci. 19 : 7 645– 47 [Google Scholar]
• Watkins CJ , Dayan P. 1992 . Q-learning. Mach. Learn. 8 : 279– 92 [Google Scholar]
• Weiner B. 1995 . Judgments of Responsibility: A Foundation for a Theory of Social Conduct New York: Guilford [Google Scholar]
• Wu CM , Vélez N , Cushman FA. 2022 . Representational exchange in human social learning: balancing efficiency and flexibility. The Drive for Knowledge: The Science of Human Information Seeking I Cogliati Dezza, E Schulz, C Wu 169– 92 . Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Young L , Cushman F , Hauser M , Saxe R. 2007 . The neural basis of the interaction between theory of mind and moral judgment. PNAS 104 : 20 8235– 40 [Google Scholar]
• Yu H , Siegel JZ , Clithero JA , Crockett MJ. 2021 . How peer influence shapes value computation in moral decision-making. Cognition 211 : 104641 [Google Scholar]
• Yu H , Siegel JZ , Crockett MJ. 2019 . Modeling morality in 3-D: decision-making, judgment, and inference. Top. Cogn. Sci. 11 : 2 409– 32 [Google Scholar]
• Zahavi A. 1975 . Mate selection—a selection for a handicap. J. Theor. Biol. 53 : 1 205– 14 [Google Scholar]
• Zaki J , Mitchell JP. 2011 . Equitable decision making is associated with neural markers of intrinsic value. PNAS 108 : 49 19761– 66 [Google Scholar]
• Zaki J , Schirmer J , Mitchell JP. 2011 . Social influence modulates the neural computation of value. Psychol. Sci. 22 : 7 894– 900 [Google Scholar]
• Article Type: Review Article

Most Read This Month

Most cited most cited rss feed, job burnout, executive functions, social cognitive theory: an agentic perspective, on happiness and human potentials: a review of research on hedonic and eudaimonic well-being, sources of method bias in social science research and recommendations on how to control it, mediation analysis, missing data analysis: making it work in the real world, grounded cognition, personality structure: emergence of the five-factor model, motivational beliefs, values, and goals.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Special  07 July 2021

Computational social science

The availability of big data has greatly expanded opportunities to study society and human behaviour through the prism of computational analyses. The resulting field is known as computational social science and is defined by its interdisciplinary approaches. However, this type of cross-discipline work is intrinsically challenging, calling for the development of new collaborations and toolkits. In this Nature special collection of articles, we explore some of the fundamental questions and opportunities in computational social science.

• Special content
• Panel at Networks 2021

Magazine content

The powers and perils of using digital data to understand human behaviour

Computational social science is a powerful research tool. But it needs its different disciplines to find a common language.

Everyone should decide how their digital data are used — not just tech companies

Smartphones, sensors and consumer habits reveal much about society. Too few people have a say in how these data are created and used.

• Jathan Sadowski
• Salomé Viljoen
• Meredith Whittaker

Food shocks and how to avoid them

Addressing the problem of sudden food scarcity in US cities, and the up-and-coming field of computational social science.

• Shamini Bundell
• Nick Petrić Howe

Perspective articles

Integrating explanation and prediction in computational social science.

The combination of computational and social sciences requires the integration of explanatory and predictive approaches into ‘integrative modelling’, according to Hofman and colleagues.

• Jake M. Hofman
• Duncan J. Watts
• Tal Yarkoni

Meaningful measures of human society in the twenty-first century

Approaches for the management, use and analysis of large-scale behavioural datasets that were not originally intended or created for research are described.

• David Lazer
• Eszter Hargittai
• Jason Radford

Measuring algorithmically infused societies

This Perspective discusses the challenges for social science practices imposed by the ubiquity of algorithms and large-scale measurement and what should—and should not—be measured in societies pervaded by algorithms.

• Claudia Wagner
• Markus Strohmaier
• Tina Eliassi-Rad

Thinking clearly about social aspects of infectious disease transmission

The use of new datastreams and local knowledge to shed light on social aspects of disease transmission will allow more accurate modelling and prediction of epidemics.

• Caroline Buckee
• Abdisalan Noor
• Lisa Sattenspiel

Human social sensing is an untapped resource for computational social science

The ability of people to understand the thoughts and actions of others—known as social sensing—can be combined with computational social science to advance research into human sociality.

• Mirta Galesic
• Wändi Bruine de Bruin
• Tamara van der Does

Explorer tool

Explore computational social science using our experimental app

Springer Nature is experimenting with new ways to summarize science and would like your feedback.

Further reading

Sixteen facial expressions occur in similar contexts worldwide

An analysis of 16 types of facial expression in thousands of contexts in millions of videos revealed fine-grained patterns in human facial expression that are preserved across the modern world.

• Alan S. Cowen
• Dacher Keltner
• Gautam Prasad

Mobility network models of COVID-19 explain inequities and inform reopening

An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.

• Serina Chang
• Emma Pierson
• Jure Leskovec

Monitoring hiring discrimination through online recruitment platforms

An analysis of the search behaviour of recruiters on a Swiss online recruitment platform shows that jobseekers from minority ethnic groups are less likely to be contacted by recruiters, and also provides evidence of gender-based discrimination.

• Dominik Hangartner
• Daniel Kopp
• Michael Siegenthaler

The online competition between pro- and anti-vaccination views

Insights into the interactions between pro- and anti-vaccination clusters on Facebook can enable policies and approaches that attempt to interrupt the shift to anti-vaccination views and persuade undecided individuals to adopt a pro-vaccination stance.

• Neil F. Johnson
• Nicolas Velásquez
• Yonatan Lupu

The scales of human mobility

A model shows that human mobility is organized within hierarchical containers that coincide with familiar scales and that a power-law distribution emerges when movements between different containers are combined.

• Laura Alessandretti
• Sune Lehmann

How Facebook, Twitter and other data troves are revolutionizing social science

A new breed of researcher is turning to computation to understand society — and then change it.

• Heidi Ledford

The ethical questions that haunt facial-recognition research

Journals and researchers are under fire for controversial studies using this technology. And a Nature survey reveals that many researchers in this field think there is a problem.

• Richard Van Noorden

Social scientists battle bots to glean insights from online chatter

Automated production of social-media posts can confound research studies.

Hierarchies defined through human mobility

An analysis of worldwide data finds that human mobility has a hierarchical structure. A proposed model that accounts for such hierarchies reproduces differences in mobility behaviour across genders and levels of urbanization.

• Elsa Arcaute

Big data and simple models used to track the spread of COVID-19 in cities

Understanding the dynamics of SARS-CoV-2 infections could help to limit viral spread. Analysing mobile-phone data to track human contacts at different city venues offers a way to model infection risks and explain infection disparities.

• Kevin C. Ma
• Marc Lipsitch

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Computational Social Science: A Literature Review

Involved persons.

• Christopher Zirnig
• Dr. rer. soc. Anne Suphan

Involved institutions

Mapping of Computational Social Science Research Themes: A Two-Decade Review

• Conference paper
• First Online: 29 May 2022
• Cite this conference paper

• Agung Purnomo 7 ,
• Nur Asitah 8 ,
• Elsa Rosyidah 8 , 9 ,
• Andre Septianto 8 &
• Mega Firdaus 8  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 289))

559 Accesses

1 Citations

Research on computational social science continues to develop but is limited to one country and/or one field. From the perspective of bibliometric reviews, this study purposes to visually research mapping and research trends in the field of computational social science on an international scale. This study used bibliometric techniques with secondary data from Scopus. This study analyzed 729 scientific documents published from 1999 to 2020. According to the research, the USA, Massachusetts Institute of Technology, and Claudio Cioffi-Revilla had the most active affiliated countries, institutions, and individual scientists in computational social science research. Based on the identification of a collection of knowledge accumulated from two decades of publication, this research proposes a grouping of computational social science research themes: computational methods, human, application of computer science, social science, and element of social media, abbreviated as CHASE research themes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Exploration Metrics Based on Scientific Mapping in the Use of Social Networks and Politics 2.0

The Interplay Between Social Science and Big Data Research: A Bibliometric Review of the Journal Big Data and Society, 2014–2021

Lazer et al. (2009): Computational Social Science

Chang, R.M., Kauffman, R.J., Kwon, Y.: Understanding the paradigm shift to computational social science in the presence of big data. Decis. Support Syst. 63 , 67–80 (2014). https://doi.org/10.1016/j.dss.2013.08.008

Article   Google Scholar  

Burger, Oz, Kennedy, C.: Computational social science of disasters: opportunities and challenges. Futur. Internet. 11 , 103 (2019). https://doi.org/10.3390/fi11050103

Edelmann, A., Wolff, T., Montagne, D., Bail, C.A.: Computational social science and sociology. Annu. Rev. Sociol. 46 , 24.1–24.21 (2020). https://doi.org/10.1146/annurev-soc-121919 054621

Tao, X., Velasquez-Silva, J.D., Liu, J., Zhong, N.: Editorial: computational social science as the ultimate web intelligence. World Wide Web 23 , 1743–1745 (2020). https://doi.org/10.1007/s11280-020-00801-2

Natali, F., Carley, K.M., Zhu, F., Huang, B.: The role of different tie strength in disseminating different topics on a microblog. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 203–207. ACM, New York, USA (2017). https://doi.org/10.1145/3110025.3110130

Peng, T.-Q., Liang, H., Zhu, J.J.H.: Introducing computational social science for Asia-Pacific communication research. Asian J. Commun. 29 , 205–216 (2019). https://doi.org/10.1080/01292986.2019.1602911

Cioffi-Revilla, C.: Computational social science. Wiley Interdiscip. Rev. Comput. Stat. 2 , 259–271 (2010). https://doi.org/10.1002/wics.95

Zhang, J., Wang, W., Xia, F., Lin, Y.-R., Tong, H.: Data-driven computational social science: a survey. Big Data Res. 21 , 100145 (2020). https://doi.org/10.1016/j.bdr.2020.100145

Xia, F., Liu, L., Jedari, B., Das, S.K.: PIS: a multi-dimensional routing protocol for socially-aware networking. IEEE Trans. Mob. Comput. 15 , 2825–2836 (2016). https://doi.org/10.1109/TMC.2016.2517649

Maulana, F.I., Zamahsari, G.K., Purnomo, A.: Web design for distance learning indonesian language BIPA. In: 2020 International Conference on Information Management and Technology (ICIMTech), pp. 988–991. IEEE, Jakarta (2020). https://doi.org/10.1109/ICIMTech50083.2020.9211175

Lazer, D.M.J., Pentland, A., Watts, D.J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M.J., Strohmaier, M., Vespignani, A., Wagner, C.: Computational social science: obstacles and opportunities. Science 369 (6507), 1060–1062 (2020). https://doi.org/10.1126/science.aaz8170

Cappella, J.N.: Vectors into the future of mass and interpersonal communication research: big data, social media, and computational social science. Hum. Commun. Res. 43 , 545–558 (2017). https://doi.org/10.1111/hcre.12114

Garcia-Mancilla, J., Ramirez-Marquez, J.E., Lipizzi, C., Vesonder, G.T., Gonzalez, V.M.: Characterizing negative sentiments in at-risk populations via crowd computing: a computational social science approach. Int. J. Data Sci. Anal. 7 , 165–177 (2019). https://doi.org/10.1007/s41060-018-0135-9

Gaffney, D., Matias, J.N.: Caveat emptor, computational social science: large-scale missing data in a widely-published Reddit corpus. PLoS ONE 13 , e0200162 (2018). https://doi.org/10.1371/journal.pone.0200162

IGI Global: What is Bibliometric? https://www.igi-global.com/dictionary/bibliometric/49021

Purnomo, A., Asitah, N.: Publication dataset of computational social science (1999–2020), https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: https://doi.org/10.7910/DVN/8R9JRO . https://doi.org/10.7910/DVN/8R9JRO

Purnomo, A., Sari, Y.K.P., Firdaus, M., Anam, F., Royidah, E.: Digital literacy research: a scientometric mapping over the past 22 years. In: International Conference on Information Management and Technology (ICIMTech), pp. 108–113. IEEE (2020). https://doi.org/10.1109/ICIMTech50083.2020.9211267

Purnomo, A., Rosyidah, E., Firdaus, M., Asitah, N., Septianto, A.: Data science publication: thirty-six years lesson of scientometric review. Int. Conf. Inf. Manag. Technol. 893–898 (2020). https://doi.org/10.1109/ICIMTech50083.2020.9211192

Ranjbar-Sahraei, B., Negenborn, R.R.: Research positioning and trend identification. TU Delft, Walanda (2017)

Google Scholar  

van Eck, N.J., Waltman, L.: Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 , 523–538 (2010). https://doi.org/10.1007/s11192-009-0146-3

Download references

Author information

Authors and affiliations.

Entrepreneurship Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta, 11480, Indonesia

Agung Purnomo

Institute for Research and Community Services, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, 61218, Indonesia

Nur Asitah, Elsa Rosyidah, Andre Septianto & Mega Firdaus

Environmental Engineering Department, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, 61218, Indonesia

Elsa Rosyidah

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Agung Purnomo .

Editor information

Editors and affiliations.

Malla Reddy University, Hyderabad, India

V. Sivakumar Reddy

Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, India

V. Kamakshi Prasad

Federation University at IIBIT, Sydney, NSW, Australia

D. N. Mallikarjuna Rao

KIIT University, Bhubaneswar, India

Suresh Chandra Satapathy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Purnomo, A., Asitah, N., Rosyidah, E., Septianto, A., Firdaus, M. (2022). Mapping of Computational Social Science Research Themes: A Two-Decade Review. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_55

Download citation

DOI : https://doi.org/10.1007/978-981-19-0011-2_55

Published : 29 May 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-0010-5

Online ISBN : 978-981-19-0011-2

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• PLoS Comput Biol
• v.9(7); 2013 Jul

Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

Loading metrics

Open Access

Peer-reviewed

Research Article

Perpetrators of gender-based workplace violence amongst nurses and physicians–A scoping review of the literature

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada

Roles Data curation, Formal analysis, Investigation, Validation, Writing – review & editing

Affiliation Princess Margaret Cancer Centre, Toronto, Canada

Roles Conceptualization, Investigation, Methodology, Validation, Writing – review & editing

Roles Formal analysis, Investigation, Writing – original draft

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing

• Basnama Ayaz, 
• Graham Dozois, 
• Andrea L. Baumann, 
• Adam Fuseini, 
• Sioban Nelson

• Published: September 6, 2024
• https://doi.org/10.1371/journal.pgph.0003646
• Peer Review
• Reader Comments

In healthcare settings worldwide, workplace violence (WPV) has been extensively studied. However, significantly less is known about gender-based WPV and the characteristics of perpetrators. We conducted a comprehensive scoping review on Type II (directed by consumers) and Type III (perpetuated by healthcare workers) gender based-WPV among nurses and physicians globally. For the review, we followed the Preferred Reporting Items for Systematic and Meta Analyses extension for Scoping Review (PRISMA-ScR). The protocol for the comprehensive review was registered on the Open Science Framework on January 14, 2022, at https://osf.io/t4pfb/ . A systematic search in five health and social science databases yielded 178 relevant studies that indicated types of perpetrators, with only 34 providing descriptive data for perpetrators’ gender. Across both types of WPV, men (65.1%) were more frequently responsible for perpetuating WPV compared to women (28.2%) and both genders (6.7%). Type II WPV, demonstrated a higher incidence of violence against women; linked to the gendered roles, stereotypes, and societal expectations that allocate specific responsibilities based on gender. Type III WPV was further categorized into Type III-A (horizontal) and Type III-B (vertical). With Type III WPV, gendered power structures and stereotypes contributed to a permissive environment for violence by men and women that victimized more women. These revelations emphasize the pressing need for gender-sensitive strategies for addressing WPV within the healthcare sector. Policymakers must prioritize the security of healthcare workers, especially women, through reforms and zero-tolerance policies. Promoting gender equality and empowerment within the workforce and leadership is pivotal. Additionally, creating a culture of inclusivity, support, and respect, led by senior leadership, acknowledging WPV as a structural issue and enabling an open dialogue across all levels are essential for combating this pervasive problem.

Citation: Ayaz B, Dozois G, Baumann AL, Fuseini A, Nelson S (2024) Perpetrators of gender-based workplace violence amongst nurses and physicians–A scoping review of the literature. PLOS Glob Public Health 4(9): e0003646. https://doi.org/10.1371/journal.pgph.0003646

Editor: Tanmay Bagade, The University of Newcastle Australia: University of Newcastle, AUSTRALIA

Received: May 22, 2024; Accepted: August 2, 2024; Published: September 6, 2024

Copyright: © 2024 Ayaz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are included in the manuscript and its supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The International Labour Office (ILO), the International Council of Nurses (ICN), the World Health Organization (WHO), and Public Services International (PSI) defined WPV as "incidents where staff are abused, threatened or assaulted in circumstances related to their work, including commuting to and from work, involving an explicit or implicit challenge to their safety, well-being or health" [ 1 ]. The ILO [ 2 ] further defined "gender-based violence and harassment means violence and harassment directed at persons because of their sex or gender, or affecting persons of a particular sex or gender disproportionately".

Irrespective of industry, workplace violence (WPV) can cause lasting trauma and injuries and is a serious threat to human and health resources. WPV includes physical and psychological violence, including physical assault, verbal abuse, sexual harassment, and bullying. Gender-based workplace violence (GB-WPV), which is experienced across operational layers of an organization (horizontal) and organizational hierarchy (vertical), reinforces the differential risk for exposure and outcomes of violence for men and women [ 3 ]. Despite extensive research on workplace violence in healthcare, GB-WPV, its perpetrators, and its impact on healthcare professionals remains understudied. We presented the sex-segregated prevalence and risk factors for WPV somewhere else [ 4 ]. The earlier paper focused on the scope and scale of workplace violence (WPV), risk factors and its impact on men and women. As part of the same scoping review protocol, this paper reports on GB-WPV perpetrators. It specifically focuses on explaining the root causes of violent acts by individuals and the triggers and circumstances to provide gender-sensitive recommendations.

A systematic review [ 5 ] of the consequences of exposure to WPV in the healthcare setting based on 68 studies reported psychological and emotional effects such as post-traumatic stress, depression, anger, and fear. These effects impact work productivity, leading to increased sick leaves, poor job satisfaction, burnout, and higher attrition rates, particularly for women [ 5 ]. Studies have also shown that men are more likely to commit physical violence [ 6 ] and sexual harassment [ 7 , 8 ], while women are more often engaged in verbal abuse [ 9 ]. Moreover, gender stereotypes and inequalities in the distribution of roles and responsibilities can worsen power imbalances [ 3 ]. By recognizing and understanding these issues, employers and organizations can more effectively prevent and deal with gender-based workplace violence, ensuring a safer and more equitable work environment for everyone.

The classification of workplace violence has evolved, delineating distinct categories shaped by the nature of its perpetrators. The current working taxonomy categorizes WPV into four types based on the perpetrators of violence. This typology, as shown in Table 1 , emerged from a collaborative effort of a workshop on workplace violence intervention research held in Washington, DC, in 2000. The findings of this endeavour were subsequently published by the U.S. Department of Justice in 2001 [ 13 ]. Since then, this framework has been adopted by multiple organizations [ 10 , 11 , 14 ], and by researchers [ 12 , 15 ].

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pgph.0003646.t001

This paper explores the dynamics of workplace violence by categorizing and summarizing both Type II WPV, from patients and significant others, and Type III WPV (horizontal and vertical), which pertain to violence perpetuated by colleagues, supervisors, and administrators within the organization. Additionally, we explore the nuances of GB-WPV, considering both the perpetrators and nurses and physicians as victims of WPV. We summarized perpetrators based on their gender and synthesized the factors attributed to Type II and III, which are prevalent forms of violence reported in the literature. Type I and Type IV violence are beyond the scope of this paper as they focus on a security-based rather than workplace culture interventions. Understanding the factors contributing to these types of WPV is crucial to developing effective preventive interventions and strategies. Currently, there is a dearth of information identifying the characteristics of individuals who are more likely to commit GB-WPV and the characteristics of those targeted by such offences. This review addresses this gap by synthesizing data from studies that reported on the gender/sex data for various forms (please see S1 Text : Definitions of the Forms of Violence) of WPV and perpetrators of WPV among nurses and physicians.

While WPV affects individuals across the gender spectrum and in different professional groups, women are disproportionately affected. Some studies attributed it to their preponderance in the health workforce, their overrepresentation in lower positions in organizational and professional hierarchies, and societal gender norms in most cultures that subjugate women [ 9 , 16 ]. Recognizing that workplace violence is fundamentally intertwined with broader societal structures rooted in socioeconomic, cultural, and institutional factors, we underscore the necessity of a systematic approach to address this issue—one that is integrated, participatory, culturally and gender-sensitive, and non-discriminatory [ 1 ]. While current interventions aimed at addressing WPV primarily focus on assessing the effectiveness of training interventions to prevent and manage WPV in healthcare settings [ 17 – 19 ], they often lack gender-segregated findings for their effectiveness. Clarifying the existing situation on the gender of victims and perpetrators for specific Type/s of violence would help develop gender-sensitive interventions and policies to more effectively address WPV. This scoping review focuses on understanding GB-WPV and its perpetrators in the global health workforce, including nurses and physicians. Our preliminary search for a scoping review revealed that GB-WPV affects men, women, and non-binary persons. However, most studies included in this review reported gender as binary (men and women); only a few studies included non-binary personnel (for sample-see Table 2 , in results section). Therefore, we defined gender as a binary for this review and deliberated on it in the discussion section.

https://doi.org/10.1371/journal.pgph.0003646.t002

Our specific objectives set out for this paper were:

• Describe the proportions of WPV and related perpetrator/s contributing to Type II (from patients/clients/families) and Type III (worker-to-worker) violence among nurses and physicians in different contexts.
• Summarize the gendered perpetration of Type II and Type III WPV against men and women in the health workforce.
• Identify gaps in the state of knowledge to recommend direction for future empirical research studies.

Protocol registration and study design

Following the Joanna Briggs Institute (JBI) revised guidelines, we conducted a scoping review. The protocol for this review was registered on the Open Science Framework on January 14, 2022, and is accessible at https://osf.io/t4pfb/ and S2 Text : Registered Protocol. The scoping literature review design addressed the research questions and accommodated the heterogeneous and complex nature of the literature. This method is appropriate for exploring the extent of the literature, mapping and summarizing the evidence, and identifying and analyzing knowledge gaps to inform future research. The framework used for this review consists of eight steps; they are built upon the seminal framework of Arksey and O’Malley’s scoping review, which was further developed by Levac and colleagues. The revised guidelines of JBI align these eight steps with the Preferred Reporting Items for Systematic and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), ensuring rigour, transparency, and trustworthiness in Reporting the conduct of the scoping review. The first step of the scoping review framework is to align with research objectives, the title, and the inclusion criteria, as well as the exclusion criteria (see Box 1 ). Please see S1 Checklist : PRISMA-ScR Checklist.

Box 1. Study selection criteria.

Inclusion Criteria for Studies

1. The study participants included nurses and/ or physicians who experienced WPV during their careers.

2. Provided sex-segregated data for any form of violence and any type of perpetrators among nurses and physicians, including students, globally.

3. Published in English and after 2010.

Exclusion criteria

4. Studies that did not provide sex-segregated data and information for perpetrators.

5. Exclude systematic/ scoping reviews, concept or theoretical papers, and theses.

Search strategy

The research team collaborated with a health sciences librarian to develop a comprehensive search strategy. The systematic search focused on published literature in various databases, including Ovid MEDLINE: Epub Ahead of Print, In-Process and Other Non-Indexed Citations, which were translated in CINAHL Plus, APA PsycINFO, Web of Sciences, and Gender Studies Databases, Applied Social Sciences Index & Abstracts (ASSIA), and Sociological Abstracts ( S3 Text : Ovid MEDLINE search strategy, which was translated in all other databases). The search terms related to the population (midwifery, nursing, and physicians), concepts (violence and gender-based violence), and context (healthcare) were combined appropriately based on the scoping review objectives. These terms were identified through a preliminary literature search on various aspects of workplace violence in Google Scholar. The final search results were exported to EndNote, a citation manager, to de-duplicate sources from multiple databases. After de-duplication, the sources were imported into the Covidence online software program that streamlined the screening process by two independent reviewers. The final search of the literature review for this study was conducted on 11 th February 2024.

Evidence screening and selection

The identified sources were screened based on the inclusion criteria ( S4 Text : Excluded Sources). Two independent reviewers screened the titles and abstracts to shortlist the sources. Discrepancies were resolved through discussion and consensus, with a complete source review conducted, if necessary, followed by a full-text review against the inclusion criteria by two reviewers. The selection process is presented in the PRISMA diagram ( Fig 1 ). Given the overall objective of the review to map the most frequent forms and prevalence of GB-WPV for midwives, nurses, and physicians in different contexts and clinical settings, a quality assessment of the identified sources was not conducted.

https://doi.org/10.1371/journal.pgph.0003646.g001

Data analysis and synthesis of results

This paper is a component of a multi-part scoping review; it reports on the perpetrators of WPV from gender-segregated prevalence data reported from a global context among the health workforce, including nurses and physicians. The prevalence and risk factors have been reported elsewhere [ 4 ]. This paper reports on Type II and Type III (vertical and horizontal) WPV perpetrators. Data from all sources ( S1 Data ) that reported sex/gender segregated findings and provided information for the types of perpetrators were included in mapping the prevalence of GB-WPV (See Table 2 ) for several types/forms of WPV and the clinical setting across countries/special regions. We could not calculate a mean score for various forms of violence based on gender for all the studies that provided information on perpetrators because of the wide variability in the operational definitions of the terms and the concepts in these studies. These studies also did not consistently provide quantifiable data for the Types of perpetrators. Only 34 studies (19%) provided the gender of perpetrators. We summarized the proportion of male and female perpetrators in those studies for Type II, Type III-A (horizontal) and Type III-B (Vertical) violence (see Table 3 ).

https://doi.org/10.1371/journal.pgph.0003646.t003

After de-duplication, 8435 possible references were imported for screening in the Covidence. These studies were screened against the title by one person, 1551 were shortlisted to be screened (for title and abstract) by two independent reviewers, and 402 were assessed for full-text eligibility. After applying the inclusion and exclusion criteria, 178 [ 6 – 9 , 15 , 20 – 125 ] studies were retained (PRISMA diagram, Fig 1 ) and analyzed to report on perpetrators that provided gender-segregated findings for WPV and information on various types of perpetrators ( Table 2 ). We included studies published between 2010–2024. The most common study design was quantitative, cross-sectional (n = 168), mixed methods (n = 4), and qualitative methods (n = 6).

Perpetrators for the three types of violence

A total of 178 studies provided information on the perpetrators of either Type II (consumers/patients, including patients’ companions), Type III-A (from colleagues), and Type III-B (from administrators and superior authorities within and between professions) violence. Studies included in this review did not consistently provide data for all types of violence and perpetrators; instead, they provided data for any Type/s. Of 178 studies, 141 (79%) reported perpetrators for Type II violence, followed by 93 (52.2%) for Type III-B (vertical) and 92 (51.6%) for Type III-A (horizontal) violence. Only 40 (22.5%) studies [ 9 , 21 – 53 , 164 , 168 , 172 , 177 , 178 , 191 ] reported information about all three types of violence.

While the search terms yielded many studies, there was significantly less information on the gender of perpetrators of WPV. Of the 178 studies reported on perpetrators, only 34 studies provided data for perpetrators’ gender (detailed in Table 3 ). Across the three types of violence, more men (65%) were responsible for perpetrating WPV compared to women (28%). Both men and women perpetuated violence in the remaining 7% of cases. Of the 34 studies, 25 studies reported on Type II violence, predominantly perpetrated by men, encompassing general violence [ 37 , 40 , 53 – 60 ], physical violence [ 6 , 35 , 44 , 61 – 63 , 178 ], verbal violence [ 6 , 35 , 44 , 53 , 63 , 172 , 178 ], and sexual harassment [ 8 , 41 , 44 , 63 , 64 ]. In most of these studies, women experienced a higher prevalence of violence than men. Gender-based workplace violence against nurses emerged as a pressing issue for Type II (56.2%) violence in ten studies [ 6 , 8 , 35 , 40 , 53 – 56 , 168 , 178 ]; men perpetrated 80% of the violence while women were responsible for only 19% violence, and almost all studies reported a higher prevalence of WPV against female nurses. A recent study [ 168 ] from 79 countries, though reported gender was not significant for WPV, being a nurse had higher odds of experiencing WPV (OR = 1.95; 95% CI 1.46 to 2.59, p<0.001) than a physician (OR = 1.70; 95% CI 1.33 to 2.18, p<0.001). In this study, most perpetrators were consumers (56%), followed by supervisors (16%) and colleagues (9%), or a combination of all (19%).

Violence perpetrated by colleagues (Type III-A) was reported by 15 studies, including seven for physicians [ 41 , 43 , 65 – 68 , 172 ], three studies for nurses [ 40 , 53 , 178 ], and five that included both professionals [ 29 , 37 , 44 , 60 , 63 ]. Approximately 24% of violence was perpetrated by colleagues (Type III-A) among nurses and physicians. More perpetrators were men (63.5%) than women (23%), and some violence by colleagues was reported as perpetrated by both men and women (13.5%). Only one study [ 29 ] reported higher rates of bullying by women (37.9%) than men (10.5%) and by both genders (51.6%). Two other studies reported higher mobbing behaviours (20% Vs. 69%) (192) and (8%vs.93%) (72) by women. In these studies, most perpetrators (40.7%) were supervisors and senior colleagues (Type III-B). Victims were both physicians (53.1%) and nurses (53.6%) with similar intensity, but a higher number of women (n = 195, 56.4%) were exposed to bullying than men (n = 18, 36%). Additionally, those who experienced bullying had lower levels of psychological health status. Bullying from colleagues (26.4%) and patients/consumers (7.7%) was perceived as less harmful than bullying from supervisors (Type III-B), which was also less reported because of the fear of consequences.

Of the 34 studies reporting on the gendered perpetuation of WPV ( Table 3 ), 24 reported on Type III-B (vertical) violence, which was more prevalent among physicians (51.5%) than nurses (16%). When it did occur among nurses, more men (77%) perpetuated Type III-B violence than women (18%) and both men and women (5%). Several studies highlighted physicians as perpetrators of WPV against nurses regardless of gender [ 8 , 51 , 53 ]. Similarly, more men (67.5%) than women (24.2%) and both genders (8.2%) perpetuated Type III-B violence among physicians. In seven of ten studies (70%) for Type III-B violence among physicians, male supervisors and administrators perpetuated sexual harassment [ 41 , 64 – 69 ]. Four studies reported bullying [ 43 , 70 , 172 ] and emotional abuse [ 71 ], which was also perpetrated by men.

Medical residents appear to be particularly vulnerable to Type III-B violence, with more than 60% of studies [ 41 , 43 , 64 , 66 , 67 , 71 , 172 , 175 ] reporting this type of violence in medical residency programs. Furthermore, several studies highlighted that the perpetrator of sexual harassment was most often of the opposite sex [ 63 , 64 , 66 ]. For instance, Freedman-Weiss et al. [ 66 ] reported that male residents experienced 65.9% of harassment from men compared to 81.8% from women. On the other hand, female residents reported experiencing more harassment from men (97.7%) compared to women (42.4%). In the same study, the main perpetrators for female resident victims were attending physicians (72.9%), followed by nurses (68.5%), senior colleagues (44.7%) and same-level residents (23.5%). Among male residents, nurses were the most common perpetrator of WPV (69%), followed by attending physicians (62%), senior colleagues (41.9%) and same-level residents (25.6%).

Healthcare professionals in lower hierarchical positions, such as nurses and residents, often contend with stressful conditions and managerial or administrative abuse and harassment, posing challenges to patient care, institutional integrity, and the healthcare system. These experiences also detrimentally impact the victims’ health and career progression. For instance, Tekin and Bulut [ 51 ] found that Turkish nurses who experienced Type III-B violence reported feelings of anger, humiliation, confusion and sadness. Moreover, these experiences also led to strained relationships with others, decreased performance, and caused them to consider leaving the profession. Although this study did not specify the gender of the offender, women experienced significantly higher verbal abuse. The highest perpetuation for all forms of abuse, including verbal (85.7%), physical (46.4%) and sexual (94.4%), was from physicians. In these cases, gender and status within the organizational hierarchy played a critical role in perpetuating Type III-A and III-B WPV, which requires serious attention from employers and health organizations to address GB-WPV through a gender-sensitive approach.

Our examination explores the complexities of gender dynamics concerning both the perpetrator and the victims of workplace violence within the global healthcare community, mainly focusing on nurses and physicians. While 178 studies provided information about perpetrators and sex-segregated findings for workplace violence, only 34 studies (19%) reported the gender of the perpetrator for Type II and Type III violence. These findings provided insights into how gender and an individual’s position within the organization create unique vulnerabilities to WPV. The consequences of such violence against health workers not only affect patient care but also have broader implications for healthcare organizations and workforce landscapes. In our review, men were found to be the primary instigators, accounting for 65% of incidences of WPV, while women were responsible for 28% of instances. Both men and women perpetrated the remaining 7% of incidents. Additionally, our analysis identified distinctive behaviour patterns among male and female offenders. Recognizing that each type of violence requires a different approach for its management and prevention, we will discuss the divergent behavioural patterns of men and women perpetrators of Type II and Type III violence. We examine the underlying factors contributing to these differences and discuss the implications of adopting gender-sensitive approaches to prevent and manage GB-WPV.

Type II WPV- Client/patient

Of the 34 studies that provided the gender of perpetrators for any type/s of violence, the majority (74%, n = 25) reported on Type II WPV perpetrated by patients, their families, or visitors. In this context, male perpetrators were more prevalent, targeting both nurses (77.9%) and physicians (70%). The majority of studies that reported on Type II violence indicated a higher prevalence of various forms of violence against female nurses and physicians. The higher perpetration of WPV by men can be linked to societal norms associating aggression and dominance with masculinity [ 193 ]. At the same time, violence against a feminized nursing workforce is normalized as part of the job [ 24 , 75 , 98 , 193 ]. This link between societal norms and assigned roles was evident in several studies [ 76 , 125 ], which is deliberated in the following section.

Type II violence typically targets healthcare providers in the performance of their professional duties and is characterized by acts of physical violence [ 6 , 35 , 44 , 61 – 63 , 166 ]; verbal violence [ 6 , 35 , 44 , 53 , 63 ], and sexual harassment/ violence [ 8 , 41 , 44 , 63 , 64 , 167 ]. Most of these studies reported a higher prevalence of WPV for women for all forms of violence [ 8 , 9 , 44 , 53 , 62 , 64 , 169 , 174 , 176 ]. The social norms, which stem from social relations dictate gender roles and responsibilities, and healthcare institutions are no exception to these forces. For example, a study conducted in Italy that included all areas of practice and the entire health workforce, investigating determinants of aggression against the health workforce reported women were 1.37 times more likely to experience aggression from consumers and colleagues. In this study, nurses experienced the highest number of episodes of violence (64%). Most of these aggressive acts occurred during assistance and supportive care to patients (38%) [ 125 ]. On the other hand, men were not immune to WPV, particularly physical [ 44 , 61 , 166 , 185 ] and both physical and verbal violence in the emergency department in Saudi Arabia, Turkey and China [ 37 , 59 , 189 ]. In Turkey, male physicians experienced higher violence (62.4%) in contrast to their female counterparts (37.6%) [ 59 ]. A similar pattern emerged in Saudi Arabia, with male physicians and nurses reporting a higher prevalence (57.8%), than their female counterparts (42.8%) [ 37 ]. These three studies identified several factors for the high occurrence of WPV from patients and their relatives, including dissatisfaction with the treatment, long wait times and lack of staff [ 37 , 59 ], overcrowding and lack of security [ 37 ]. Though these highlighted factors are important to explain the occurrence of workplace violence for both men and women in the workforce, in the Saudi context, culture seems to have a protective factor for women, where public abuse from men is socially unacceptable [ 88 ]. Similarly, three other studies in Jordan attributed the higher prevalence among male physicians to culture and the existence of laws that intensify legal penalties against women abusers [ 87 ], the cultural norm of altruism and tolerance towards females, particularly physical violence [ 42 ], and a lack of encouragement for reporting WPV by females as part of the male-dominant culture [ 150 ]. Additionally, the higher occurrence of physical violence for men can also be explained by the cultural expectation of masculinity.

In contrast, women’s experience of severe sexual harassment was associated with pregnancy, family responsibilities, and occupational segregation [ 63 ]. Newman et al. [ 63 ] explained that occupational segregation also creates a vertical hierarchy where women are assigned to lower-level tasks (typically front-line care providers). The WHO report analyzed gender and equity in the health and social workforce ‘delivered by women, led by men’ (2019) and acknowledged occupational segregation as universal, which is reinforced by the broader societal norms and creates discriminatory practices with regard to gender and occupational roles [ 194 ]. In these lower positions, women experience sexual harassment from male colleagues, male patients and community members [ 16 , 194 ]. Considering the prevalence of Type II violence for both men and women linked to sex-segregated responsibilities and societal structures. Jafree [ 195 ] calls on policymakers to ensure security and protection for the health workforce, particularly women; legislative reforms for healthcare governance and zero-tolerance policies for violence were also recommended. Several other sources, too, advocate for zero-tolerance policies and emphasize the need for a managerial approach that takes all complaints seriously, reports investigation outcomes, and enforces sanctions to eliminate impunity [ 9 , 92 , 131 ]. Collaborative community efforts are required to acknowledge and alter the patriarchal culture and reduce violence against women by creating awareness about the public role through various forums, including the media [ 28 , 79 , 94 , 195 ].

Several contributing factors have been identified in the context of Type II WPV, such as noise levels, inadequate communication skills [ 74 ], perceived/actual staff incompetence or unsympathetic attitudes, dissatisfaction with service provision, prolonged wait times, and poor communication [ 53 , 196 ]. These circumstances can escalate emotions and increase the likelihood of violent encounters. Furthermore, specific treatment specialties, such as emergency departments [ 35 , 75 , 191 ], psychiatric units [ 76 , 77 ], and geriatric care [ 26 , 76 ], have demonstrated a higher risk of Type II workplace violence. Factors specific to these settings include a lack of privacy and personal space, unrealistic expectations of clients, insufficient staffing and resources, poor staff skills mix, healthcare systems and processes not understood by clients, perceived favouritism, overcrowding in emergency departments, delays in providing analgesia, and inflexible visiting hours [ 196 ]. These challenges, compounded by a shortage of skilled professionals, unclear expectations and communication, scheduling issues, and environmental stressors can generate increased stress and, thus, uncertainty. Addressing these factors constitutes the initial step in decreasing or eliminating the risk of violence for both men and women [ 197 ].

Both primary research and systematic reviews have acknowledged the difficulty associated with addressing multifactorial violence, given the diversity in population and setting and the types/classifications of violence [ 94 , 95 , 102 , 198 ]. However, these sources did not provide information about perpetuators, particularly gendered nature. For instance, a recent umbrella review examined 32 systematic reviews for WPV prevalence and characteristics. This comprehensive assessment reported that the overall prevalence from the meta-analysis of 11 reviews was 57.9%, ranging from 34.1% to 78.9% among healthcare providers and most affected were nurses working in psychiatric wards [ 198 ]. This prevalence aligns with the findings of this review. Of note, the umbrella review too did not provide information on perpetrators and prevalence based on gender and stated that the included reviews had reported variable results for men and women; however, it did underscore how gender imbalances in emergency departments could increase the risk of violence among women. Several studies in our review recommended ensuring gender equality in the health workforce and leadership positions to reduce the prevalence of WPV among women [ 9 , 30 , 63 , 80 ].

Type III WPV-Worker-on-Worker

Type III-A (Horizontal or lateral) workplace violence perpetrated by one healthcare worker against another may stem from interpersonal conflicts, workplace stress [ 12 ], or other factors contributing to a hostile work environment. Among studies that provided data on Type III-A violence, most perpetrators were men (63.5%) compared to women (23%). Horizontal WPV was reported more frequently by physicians [ 41 , 43 , 65 – 69 ] than among nurses [ 40 , 53 ]. The studies that sampled both nurses and physicians [ 29 , 37 , 44 , 60 , 63 ] also reported that men perpetuated all forms of violence in most cases for both male and female victims [ 37 , 44 , 63 ]. In some instances, women experienced violence from both men and women [ 63 ].

Type III violence is also rooted in cultural norms and societal expectations that allocate roles and responsibilities based on gender [ 63 ]; in most cultures, women are responsible for childbearing and rearing and men hold decision-making positions. This phenomenon transcends the household and is also seen in the workplace and healthcare institutions [ 9 , 68 , 78 ]. These gendered roles and responsibilities often position men in leadership positions while women are assigned to caring roles with less authority and responsibility, perpetuating discriminatory practices that negatively impact women [ 9 , 63 , 70 ]. This dynamic prevails in both wealthy and lower- and middle-income countries in varied behaviors. For instance, in Australia and New Zealand, women experienced significantly higher discrimination (31% vs. 8%) and sexual harassment (23% vs. 0.5%) than men, primarily due to family responsibilities, lack of mentorship and rigid promotion criteria [ 70 ]. In Rwanda, women’s experiences of childbearing and care, including managing pregnancy, motherhood and work, and the widespread negative stereotyping of women at work led to discrimination that co-occurred with sexual harassment within health workplaces [ 63 ]. Jacobson et al. [ 12 ] report on Type III-A violence in medical residency programs, and women experienced a significantly higher frequency of work-related incidents from colleagues and support staff, explaining the higher workload for women due to the coexistence of family responsibilities. Additionally, relational and managerial issues, including organizational affairs within large, complex health organizations, shifting duties and cohabitation of various teams on the same unit, were identified as factors contributing to Type III-A violence in Italy [ 53 ].

This type of interpersonal violence, including violence against women, is prevalent in science, technology, engineering and math (STEM), which are considered male-dominant disciplines [ 199 , 200 ], unlike healthcare, where 70% of the workforce globally are women and higher rates of violence are associated with their roles and responsibilities and the gendered workplace hierarchy [ 194 ]. In STEM, violence against women can be explained by the backlash effect, in which gender equality is associated with higher prevalence [ 200 ].

Given the social reality of women’s lives and career development in healthcare, flexible human resource development and management policies could empower women to balance their work and family responsibilities. Zampieroni et al. [ 53 ] recommend adopting realistic workloads and skill-mixed staffing, promoting gender equality in staff allocation, and participatory leadership to overcome relational conflict and managerial actions that enhance working conditions. Nurse managers must play the role of cultural gatekeepers, hold individuals accountable and foster staff empowerment; utilizing research-informed methods such as ‘cognitive rehearsal and crucial conversations’ [ 20 ] and conducting team-building workshops will assist in mitigating the impact of horizontal violence [ 21 ].

Type III-B (vertical) violence is primarily perpetrated by senior colleagues, supervisors, and administrative personnel occupying higher positions in the organizational hierarchy than the victim. Among the 34 studies, 66% reported perpetrators’ gender for Type III-B violence; men perpetuated 77% among nurses and 67.5% among physicians. The causative factors for vertical violence included organizational structure, leadership and administrative authorities, and power struggles in the health workplaces. These factors not only perpetuated WPV but also prohibited reporting of the instances due to fear of reprisal [ 29 , 63 , 66 ]. Two prevalent forms of violence linked to hierarchical/ organizational structure were sexual harassment and bullying/mobbing. The majority of studies reporting Type III-B violence reported sexual violence from male supervisors and administrators [ 8 , 41 , 44 , 63 – 69 ], particularly in medical residency programs—placing these trainee residents in a vulnerable position [ 41 , 66 , 67 ]. Additionally, vertical violence was the only type reported to be perpetuated by women at higher levels in the organizational hierarchy, particularly bullying (women: 37.9% vs. men: 10.5%) among nurses and physicians [ 29 ]. Additionally, two studies reported higher rates of mobbing behaviours by women than men among healthcare professionals, including nurses and physicians [ 72 , 192 ].

Type III-B violence is emblematic of the hierarchical and inflexible organizational culture historically dominated by male medical professionals. This stemmed from beliefs and negative stereotypes, such as women being weak, unwilling to speak up, indecisive and incompetent [ 63 ]. Additionally, perceived competence was expressed as a predictor for bullying among women [ 42 , 153 ]. Such perceptions reinforce the structural power held by men, particularly with male managers and physicians. The patriarchal institutional structures provide power domination among women as well, who could use their power to oppress individuals under their control. A qualitative study [ 201 ] from Estonia exposed this dynamic of domination and sexual harassment among nurses; it highlighted the association between power and the use of sexualized language. A female nurse stated, “I am more disturbed by their patronizing behaviour"; the nurse characterizes physicians’ attitude as: “I am a man, I am a physician, I can do and say whatever comes to my mind” (Nurse 18, p.30). Lamesoo [ 201 ] further explained that nurses placed themselves in the hospital hierarchy between physicians and patients and acknowledged that they could not challenge a physician’s incivility. However, these nurses can easily ask patients to refrain from such behaviour without hesitation because patients have less power than nurses, and patients are expected to follow hospital rules [ 201 ] dictated by nurses. These instances explain organizational power as a protective factor for offenders. However, women’s underrepresentation in positions of power places them in a vulnerable position.

Another qualitative study in Uganda by Newman et al. [ 9 ] reported from key informant interviews in the Uganda health system that "we have women over-represented in the bottom of any organization and for the men, it is an upward or inverted pyramid whereby as you go up the power ladder…. There is a tendency to abuse that power and they don’t even think that they are abusing it because they have grown up thinking they may be flattering the women…". The authors further stated that "Sexual coercion started during recruitment of health workers and continued after hiring, perpetrated by men in hierarchically superior decision-making positions supervisors, senior managers (including human resources) or medical superintendents” [ 9 ]. These severe human rights violations necessitate a transformation in the mindset of individuals in the workforce and a cultural shift at organizational levels to rectify the dominant, hierarchical and permissive environment [ 65 ]. Ensuring gender equality at the upper echelons of healthcare organizations and in decision-making positions is crucial to establishing a secure and equitable environment for all, regardless of gender. A scoping review of three evidence-based guidelines and 33 systematic reviews on strategies to prevent and manage WPV in healthcare settings reported a correlation between strong leadership to cultivate and enforce a culture of inclusivity, support and respect as a prerequisite for successful prevention of WPV [ 202 ]. Therefore, healthcare organizations’ leadership must proactively seek organizational solutions to end gender-based WPV and prioritize gender equality and protecting employees’ rights as part of their human resources for health (HRH) policy [ 9 ].

Sexual harassment in academia was found to be an issue across various contexts, particularly among women in medical residency programs. A study [ 78 ] in a U.S. medical college reported that one-third of respondents experienced sexual harassment, including medical students (51.7%), residents/fellows (31%) and faculty members (25%), which was inversely proportional to their position in the program. Similarly, sexual harassment was more prevalent among women in vascular surgery in the U.S. [ 67 ], ophthalmology in Australia and New Zealand [ 79 ] and cardiothoracic surgery, reported by a global survey [ 28 ], and rates of sexual harassment in almost all contexts were higher among female trainees. In one instance, in the U.S., male (70%) and female (69%) residents [ 41 ] in obstetrics and gynecology residents experienced sexual harassment at similar levels [ 41 ]. Additionally, one study in the U.S. with a large, representative sample (n = 6000) from a national survey reported that higher women’s representation within a specialty was associated with lower sexual harassment for both men and women from coworkers and patients [ 80 ]. This observation held true in the Canadian context where reporting of sexual harassment incidents was low (2.9%) in a study with female participants constituting 53% of the sample [ 33 ]. These women did report slightly higher rates of intimidation, harassment, and discrimination (IHD) based on gender (males 40.4%; females 48.0%). Hence, findings underscore the recurring recommendation of gender equality in the health workforce and leadership positions and the role of leadership in preventing Type II and Type III violence, including harassment.

Acknowledging sexual harassment as a prevalent problem is the crucial initial step in formulating a successful strategy to prevent its occurrence [ 65 , 203 ]; a comprehensive strategy should encompass a zero-tolerance statement across the specialty with a transparent and fair mechanism for reporting sexual harassment [ 65 , 78 ]. Moreover, it is essential to provide trainees with both direct face-to-face and electronic routes for anonymous and confidential reporting to alleviate concerns related to personal reattribution and academic detriment [ 64 , 78 ]. Standardized, transparent reporting mechanisms with well-delineated consequences for the offender must be established. Additionally, the institutions should ensure the availability of links to all the required resources is the first hit on online searches, displaying posters/presentations/ads [ 78 ].

Recognizing harassment as an institutional structural issue, senior leadership can have a protective role by serving as role models. A qualitative study conducted in Germany [ 197 ] representing women nurses (50%) and physicians (50%) explored preventive options for sexual harassment in academia. The findings revealed that leadership commitment and clear statements can significantly influence multiple levels by demonstrating openness to address taboo topics, raise awareness, and place the issue at the decision table. A participant stated, "A culture of political correctness is communicated from the top down, with the management committee and senior management acting as role models” (p.12). Another participant stated, "It is the senior staff that creates a team culture that should be supportive and transparent, with clear boundaries… .. I have an open door and open eyes policy and try to initiate rituals that allow us to work together in the correct way” (p.12). While commitment and stated actions are essential, meaningful cultural change necessitates the consistent, active, structured, and continued engagement of all health workforce members, including students and trainees, staff and especially from senior leadership. Senior leadership must be actively engaged in this process, particularly male leaders. Therefore, engaging individuals at various levels in open, nonjudgmental conversations is paramount to breaking the silence [ 30 ] and ingraining these principles into the organizational culture.

Limitations

First, in our comprehensive review of workplace violence (WPV), not all studies reported on perpetrators of WPV. Therefore, we included all studies that indicated perpetrators/ sources of violence. We categorized these sources into distinctive categories of Type II and Type III WPV. Limitations to this approach include the heterogeneity of the forms of violence reported by the included studies according to gender. While studies reported victims’ exposure to Types II, and/or III, the gender of perpetrators in each case was not specified. As a result, we presented the prevalence of the various forms and categorized the perpetrators’ type for all the studies (178) in Table 2 . The final set of studies (19%) that reported on the gender of the perpetrators was analyzed. Since fewer studies provided information about the gender of perpetrators across the types/forms of violence, future research must focus on conducting and reporting gender-segregated findings for perpetrators that will strengthen recognition of the gender-based WPV and could lead to gender-sensitive strategies at the local and international levels. Another limitation of our review was that most of the included studies operationalized gender as a binary. A few studies included either non-binary (less than 4%) [ 80 , 98 ] individuals or mentioned as others (less than 4%) [ 28 , 30 ] or unknown (less than 9%) [ 85 , 128 ], in the analysis of the total population, reported in Table 2 . Even these studies did not report findings for those minority populations or address it as a limitation. Therefore, we reported findings based on gender binary. All these studies, which represented non-binary individuals, were conducted in the USA [ 30 , 80 , 98 , 128 ] and Canada [ 85 ]; in these contexts, gender diversity and inclusion are acknowledged as compared to most Low-and -middle-income countries where sex is equated with gender. These studies did not recognize it as a limitation; only one study, which reported on survey data from the Association of American Medical Colleges (AAMC) National Sample Survey of Physicians (NSSP), expressed excluding the non-binary data because of the lower sample [ 80 ]. Considering this limitation, we recommend that future research include gender-diverse populations.

The review revealed a higher prevalence of Type II and Type III WPV among women compared to men. In parallel, it was observed that men predominantly perpetrated all forms of violence against both men and women healthcare providers. Only Type III-B violence, including bullying/ mobbing, was occasionally perpetuated by women. Both Types II and Type III violence have roots in societal structures, and women were more frequently victimized. This increased victimization of women can be attributed to their lower status in society and in the healthcare settings that assign roles and responsibilities based on this status. Additionally, women’s reproductive realities, including managing pregnancy, motherhood and work, and widespread negative stereotyping contributed to their vulnerability to gender-based WPV.

Conversely, men’s domination in leadership, decision-making and supervisory positions in most contexts creates a hierarchical and permissive environment that perpetuates violence against women. Therefore, understanding gender implications concerning both the victim and perpetrator among the critical health workforce of nurses and physicians across the globe is essential. Healthcare organizations and professional stakeholders must seriously consider zero-tolerance policies, transparent mechanisms for handling violent incidents, and the provision of appropriate support to victims. These measures will empower individual professionals, enhance patient care, and positively impact healthcare institutions and society as a whole.

Supporting information

S1 checklist. prisma-scr checklist..

https://doi.org/10.1371/journal.pgph.0003646.s001

S1 Text. Definitions of forms of workplace violence.

https://doi.org/10.1371/journal.pgph.0003646.s002

S2 Text. Registered protocol.

https://doi.org/10.1371/journal.pgph.0003646.s003

S3 Text. Ovid MEDLINE search strategy.

https://doi.org/10.1371/journal.pgph.0003646.s004

S4 Text. Sources excluded.

https://doi.org/10.1371/journal.pgph.0003646.s005

S1 Data. Data for full text review.

https://doi.org/10.1371/journal.pgph.0003646.s006

• 1. International Labour Organization, International Council of Nurses, World Health Organization, Public Services International. Framework guidelines for addressing workplace violence in the health sector. [Internet]. International Labour Office; 2002 [cited 2021 Apr 19]. Available from: http://www.icn.ch/images/stories/documents/pillars/sew/sew_framework_guidelines_for_addressing_workplace_violence.pdf
• 2. International Labour Organization. Convention C190—Violence and Harassment Convention, 2019 (No. 190) [Internet]. 2019 [cited 2024 Jul 10]. Available from: https://normlex.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C190
• View Article
• Google Scholar
• PubMed/NCBI
• 10. Public Services Health and Safety Association. Public Services Health and Safety Association | Workplace Violence—Complying with the OHS Act [Internet]. Public Services Health and Safety Association. [cited 2023 Nov 14]. Available from: https://www.pshsa.ca/resources/workplace-violence-complying-with-the-ohs-act
• 11. Registered Nurses’ Association of Ontario. Best Practice Guideline:Preventing Violence, Harassment and Bullying Against Health Workers [Internet]. RNAO; 2019 [cited 2023 Nov 14]. Available from: https://rnao.ca/bpg/guidelines/preventing-violence-harassment-and-bullying-against-health-workers
• 13. Workplace Violence: A Report to the Nation | Office of Justice Programs [Internet]. 2001 [cited 2023 Sep 27]. Available from: https://www.ojp.gov/ncjrs/virtual-library/abstracts/workplace-violence-report-nation
• 14. International Labour Organization. Statistics of work, employment and labour underutilization: 19th International Conference of Labour Statisticians [Internet]. Geneva; 2013. (ICLS/19/2013/2). Available from: https://www.ilo.org/wcmsp5/groups/public/—dgreports/—stat/documents/publication/wcms_220535.pdf
• 194. World Health Organization. Delivered by women, led by men: a gender and equity analysis of the global health and social workforce [Internet]. Geneva: World Health Organization; 2019 [cited 2023 Mar 29]. 60 p. (Human Resources for Health Observer Series;24). Available from: https://apps.who.int/iris/handle/10665/311322