misuse of social media essay pdf

Essay on Misuse of Social Media

Students are often asked to write an essay on Misuse of Social Media in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Misuse of Social Media

Introduction.

Social media is a powerful tool for communication. However, its misuse can lead to various problems.

Misuse of Social Media

It’s important to use social media responsibly. Always verify information before sharing and respect others’ privacy. Remember, misuse of social media can have serious consequences.

250 Words Essay on Misuse of Social Media

The advent of social media has revolutionized the way we communicate, making it easier to connect with people worldwide. However, its misuse has become a growing concern, with implications ranging from privacy invasion to the spread of misinformation.

Misinformation and Fake News

Privacy concerns.

Another significant issue is privacy violation. Many users unknowingly share personal information on social media, which can be exploited by malicious parties for identity theft, cyberbullying, or even cyberstalking. The lack of stringent privacy controls on some platforms exacerbates this problem.

Online Harassment and Cyberbullying

Social media platforms have unfortunately become a hotbed for online harassment and cyberbullying. These acts can have devastating psychological impacts on victims, especially among teenagers. The anonymity provided by these platforms often emboldens bullies, making it a widespread issue.

While social media has numerous benefits, its misuse presents serious challenges. It’s crucial for users to be aware of these potential pitfalls and use these platforms responsibly. Simultaneously, social media companies must take proactive steps to mitigate these issues, ensuring a safe and productive environment for all users.

500 Words Essay on Misuse of Social Media

Social media has revolutionized the way we communicate, creating a platform where information can be shared in real-time across the globe. However, with every innovation comes potential for misuse. In the context of social media, this misuse has manifested in various forms, leading to numerous societal issues.

The Spread of Misinformation

One of the most significant misuses of social media is the spread of misinformation. The open nature of these platforms allows anyone to publish content, regardless of its validity. This has led to the proliferation of ‘fake news’, which can have serious implications, from swaying public opinion during elections to creating panic during a health crisis. The speed and reach of social media amplify the impact of this misinformation, making it a significant issue in today’s digital society.

Cyberbullying and Harassment

The anonymous nature of social media has facilitated another form of misuse – cyberbullying and harassment. The ability to hide behind a screen has empowered bullies to target individuals without fear of immediate repercussions. This has led to severe psychological trauma for the victims, with some cases even leading to suicides.

Impact on Mental Health

Social media platforms are designed to be addictive, keeping users engaged for as long as possible. This has led to an increase in screen time, with detrimental effects on mental health. Studies have linked excessive social media use to increased levels of anxiety, depression, and loneliness. The pressure to present a perfect life online, coupled with the constant comparison with others, has further exacerbated these issues.

If you’re looking for more, here are essays on other interesting topics:

Leave a Reply Cancel reply

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Use and Misuse of Social Media among Indian Youth

2020, IOSR-JHSS

With its seamless reach and power of penetration among youth in our country, social media has assumed great deal of significance since its inception. Apart from providing opportunity to people for connectivity and mutual interaction, social media was found to be effective in providing opportunities for reaching out to useful information and entertainment sources and in building cultural and social capital for the user community. The proposed study attempts to explore the use and misuse of social media among youth in India.

Related Papers

Euro Asia International Journals

UCT Journal of Social Sciences and Humanities Research

Dr Mohammad Amiri

Utilization of social media is an integral part of Indian youth today. Over utilization of social media, has captured the attention of youth entirely. The dependency of youths on the social media has reached at such level that, without social media, every young person cannot think about the direction of their growth. Dependency of youth on social media is now leading to addiction. Through the several studies, it is widely accepted that over utilization of social media has profound negative influence on the Indian youth. Simultaneously, social media have also some positive effects on the life of youth. This study highlights the main purposes of utilizing social media by the youth, and attempt has been made to find out the time spent on browsing social networking sites by the youth. This study focuses on the major; and the positive and negative effects of utilizing social media on the life of youth. The result of study shows that, over utilization of social media leads youth towards addiction.

International Journal of Scientific and Engineering Research

Pradeep Krishnatray

IOSR Journals

Over the past few years, technology has advanced at a very fast pace and internet has become an inseparable part of our lives.Along with internet came social media, which is used by everyone, especially youth. Social media has created both significant new challenges and exciting opportunities. However, frequent usage of social media often has behavioral and psychological effects on the youth which may be beneficial or harmful for them. In addition to providing information and being a source of entertainment for many people, social media has some disadvantages as well. Addiction to social media is a major cause of concern along with cyber crime and various health problems.The present study was conducted to test the impact of social networking sites on the youth and various problems associated with it.

International Journal of Electronics and Information Engineering

Sam Goundar

The extensive use of Social Networking in India has been on the rise among the new generation youths. In today's world, life cannot be imagined without Facebook, YouTube, Instagram, WhatsApp, LinkedIn or Twitter accounts and online handles. The new age social networking culture has been well accepted and has met an enthusiastic response and acceptance. There are reports of cultural changes and in the way traditional interactions and social communications are conducted in India. Research studies on this new age social media impact and usage within India have been limited to speci c surveys and theories. The objectives of this study is an attempt to investigate the extent of social networking impact on the Indian youth. The reason for selecting youth as the target audience is because the direction of a country and culture is decided by the direction taken by youths of that country. This paper is an attempt to analyse the pattern of social networking usage and impact in order to determine the social networking addiction.

Dr. Ghulam Safdar

Social Networking sites provide a platform for discussion on burning issues that has been overlooked in today's scenario. This research is conducted to check the impact of social networking sites in the changing mind-set of the youth. It is survey type research and data was collected through the questionnaire. 300 sampled youth fill the questionnaire, while non-random sampling techniques was applied to select sample units. Rate of return was remaining 97 percent after fill up the questionnaire. The main objectives were as (1) To analyze the influence of social media on youth social life (2) To assess the beneficial and preferred form of social media for youth (3) To evaluate the attitude of youth towards social media and measure the spending time on social media (4) To recommend some measure for proper use of social media in right direction to inform and educate the people. Collected data was analyzed in term of frequency, percentage, and mean score of statements. Findings show that the Majority of the respondents show the agreements with these influences of social media. Respondents opine Face book as their favorite social media form, and then the like Skype as second popular form of social media, the primary place for them, 46 percent responded connect social media in educational institution computer labs, mainstream responded as informative links share, respondents Face main problem during use of social media are unwanted messages, social media is beneficial for youth in the field of education, social media deteriorating social norms, social media is affecting negatively on study of youth. Social media promotes unethical pictures, video clips and images among youth, anti-religious post and links create hatred among peoples of different communities, Negative use of social media is deteriorating the relationship among the countries, social media is playing a key role to create political awareness among youth.

Research Journey

Shubhada Kulkarni

Today social media networks such as Facebook, Twitter, Youtube, Whatsapp, Instagram etc. become an integral part of youth's life. Youth cannot imagine themselves without using social media network. They are active on social media from early in the morning to late night. Students use social media networks in the examination periods also. These new social communication channels have been adopted by all the age groups in India. Social media have a significant impact on the society especially on the youth. Social media networks have negative as well as positive impact on our society. It is important to know the positive and negative impact of social networking sites and applications on today's young generation. It is also important to know the benefits of social networking for youth. This paper is an attempt to study the impact of social networking sites and applications on young generation. It is a result of a survey conducted on youth of Jalgaon and Dhule Districts. The sample size of 100 respondents was obtained by distributing well structured questionnaires. Convenience sampling method was used. The scope of the study was limited to the youth of Jalgaon and Dhule district. The result shows that there is a significant impact of social media sites and applications on today's youth. It is also seen that there are benefits of social networks for youth. This study also describes that there were some drawbacks of social networking.

Social Networking Media: Boon or Bane?

Darshan B M

One of the best inventions of mankind is technology and more so, it is the information technology that has brought this world closer, calling it as a “Global Village”. While growth, development and speed of progress have increased due to this, a special bonding is happening between human beings. This is through the social networking. Internet has got many social networking sites such as Facebook, Twitter etc. The exposure to internet in general and social media in particular is increasing enormously. Hence this is a proper justification for probing the influence of such social media on teenagers. But here is the big question- is social networking really a boon or bane? The rapid adoption of social network sites by teenagers in India and in many other countries around the world raises some important questions. Why do teenagers’ flock to these sites? What are they expressing on them? How do these sites fit into their lives? What are they learning from their participation? Are these on-line activities like face-to-face friendships or are they different, or complementary? The objectives of this paper are to address these questions and explore their implications for teen identities. The study is based on survey method and students of Pre - University in both government and private colleges were chosen for the study as respondents. Age and sex were the demographic factors that here taken for the study. This study consisted of 100 students studying in different colleges based on systematic sampling. The subjects of interviews and quantitative results are primarily urban teenagers of Mangalore. The primary objectives of the present study is to asses overall impact of social media on teenagers. Keywords: Social media, teenagers, impact

isara solutions

International Res Jour Managt Socio Human

Social media can be concluded as any action, a platform or software tool, not to mention the way that all media have a social component. In the ongoing years, propels in Information and communications technology have foreshowed a significant change in human correspondence. Several waves of digital media, social media have introduced new communication patterns. A wide participation by the individual across the globe has widened the scope of knowledge sharing. The fruitful usage of different sorts of Social media for the advancement of social change requires a steady change of procedures to political and public setting as explicit prerequisites. The targets of this paper are to address these inquiries and investigate their suggestions for teenagers. The examination depends on primary and secondary data and taken into consideration the students studying in private and govt. colleges of Bhagalpur district. This investigation comprised of 100 samples. The subjects of meetings and quantitative outcomes have been collected from both urban and rural teenagers. The prime purpose of the current investigation is to asses by and large the effect of Social media on teenagers.

Pinaki Mandal

— Social media is one of the powerful emerging tools across the globe. India is experiencing a rapid growth in the ICT sector since 1990's and expanded since 2000. The use of social networking sites like Facebook Twitter, LinkedIn has become one of popular ways of socializing. According to the research report of PEW research centre's internet project survey 2014, the growth of cell phones, especially smart phones, has made social networking just a finger tap away. Fully 40% of cell phone owners use a social networking site on their phone, and 28% do so on a typical day. India ranks second in Facebook and third in Twitter usage. These social networking sites not only pave a way for communicating across the globe but they have played a major role in empowering women, encouraging the civic participation among women in Western, Middle East and Asian countries. This paper focuses on how social media can be used wisely to empower women in a conservative culture like India. It also discusses the pros and cons of social media participation.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Monica Munjial Singh

antwan yahyah

Rashi Ahuja

IAEME Publication

Journal ijmr.net.in(UGC Approved)

Madhavan Mani

INDIAN JOURNAL OF EXTENSION EDUCATION

SANYOGITA dhanwal

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• DOI: 10.1016/S1353-4858(16)30047-2
• Corpus ID: 41508490

Avoiding the misuse of social media by employees

• M. Taylor , J. Haggerty , +2 authors Tom Berry
• Published in Network Security 1 May 2016

11 Citations

The impact of using social networking sites at work on organizational knowledge, employee dissent on social media and organizational discipline, social media data misuse, profiling employees online: shifting public–private boundaries in organisational life, social network security risks and vulnerabilities in corporate environments, the cultural, economic and technical milieu of social media misconduct dismissals in australia and south africa, where the shoe pinches: realizing dominant problems as an organizational social media business profile evolves, social media, employee engagement, and human resource management, drivers of ewom intensity: differences between hoteliers’ perception and real reviews, mitarbeitende als botschafter von unternehmen, 26 references, employer’s use of social networking sites: a socially irresponsible practice, social media etiquette: a guide and checklist to the benefits and perils of social marketing, twitter thou doeth, legal and ethical implications of corporate social networks, social networking sites and the legal profession: balancing benefits with navigating minefields, ethical issues for internet use policy: balancing employer and employee perspectives, networked narratives: understanding word-of-mouth marketing in online communities, knowledge protection challenges of social media encountered by organizations, web 2.0’s marketing impact on low-involvement consumers, computer use monitoring and privacy at work, related papers.

Showing 1 through 3 of 0 Related Papers

Advertisement

Misinformation, manipulation, and abuse on social media in the era of COVID-19

• Published: 22 November 2020
• Volume 3 , pages 271–277, ( 2020 )

Cite this article

• Emilio Ferrara 1 ,
• Stefano Cresci 2 &
• Luca Luceri 3  

31k Accesses

95 Citations

40 Altmetric

Explore all metrics

The COVID-19 pandemic represented an unprecedented setting for the spread of online misinformation, manipulation, and abuse, with the potential to cause dramatic real-world consequences. The aim of this special issue was to collect contributions investigating issues such as the emergence of infodemics, misinformation, conspiracy theories, automation, and online harassment on the onset of the coronavirus outbreak. Articles in this collection adopt a diverse range of methods and techniques, and focus on the study of the narratives that fueled conspiracy theories, on the diffusion patterns of COVID-19 misinformation, on the global news sentiment, on hate speech and social bot interference, and on multimodal Chinese propaganda. The diversity of the methodological and scientific approaches undertaken in the aforementioned articles demonstrates the interdisciplinarity of these issues. In turn, these crucial endeavors might anticipate a growing trend of studies where diverse theories, models, and techniques will be combined to tackle the different aspects of online misinformation, manipulation, and abuse.

Similar content being viewed by others

Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation

Coronavirus Conspiracy Theories: Tracing Misinformation Trajectories from the Fringes to the Mainstream

The Role of Online Misinformation and Fake News in Ideological Polarization: Barriers, Catalysts, and Implications

Avoid common mistakes on your manuscript.

Introduction

Malicious and abusive behaviors on social media have elicited massive concerns for the negative repercussions that online activity can have on personal and collective life. The spread of false information [ 8 , 14 , 19 ] and propaganda [ 10 ], the rise of AI-manipulated multimedia [ 3 ], the presence of AI-powered automated accounts [ 9 , 12 ], and the emergence of various forms of harmful content are just a few of the several perils that social media users can—even unconsciously—encounter in the online ecosystem. In times of crisis, these issues can only get more pressing, with increased threats for everyday social media users [ 20 ]. The ongoing COVID-19 pandemic makes no exception and, due to dramatically increased information needs, represents the ideal setting for the emergence of infodemics —situations characterized by the undisciplined spread of information, including a multitude of low-credibility, fake, misleading, and unverified information [ 24 ]. In addition, malicious actors thrive on these wild situations and aim to take advantage of the resulting chaos. In such high-stakes scenarios, the downstream effects of misinformation exposure or information landscape manipulation can manifest in attitudes and behaviors with potentially dramatic public health consequences [ 4 , 21 ].

By affecting the very fabric of our socio-technical systems, these problems are intrinsically interdisciplinary and require joint efforts to investigate and address both the technical (e.g., how to thwart automated accounts and the spread of low-quality information, how to develop algorithms for detecting deception, automation, and manipulation), as well as the socio-cultural aspects (e.g., why do people believe in and share false news, how do interference campaigns evolve over time) [ 7 , 15 ]. Fortunately, in the case of COVID-19, several open datasets were promptly made available to foster research on the aforementioned matters [ 1 , 2 , 6 , 16 ]. Such assets bootstrapped the first wave of studies on the interplay between a global pandemic and online deception, manipulation, and automation.

Contributions

In light of the previous considerations, the purpose of this special issue was to collect contributions proposing models, methods, empirical findings, and intervention strategies to investigate and tackle the abuse of social media along several dimensions that include (but are not limited to) infodemics, misinformation, automation, online harassment, false information, and conspiracy theories about the COVID-19 outbreak. In particular, to protect the integrity of online discussions on social media, we aimed to stimulate contributions along two interlaced lines. On one hand, we solicited contributions to enhance the understanding on how health misinformation spreads, on the role of social media actors that play a pivotal part in the diffusion of inaccurate information, and on the impact of their interactions with organic users. On the other hand, we sought to stimulate research on the downstream effects of misinformation and manipulation on user perception of, and reaction to, the wave of questionable information they are exposed to, and on possible strategies to curb the spread of false narratives. From ten submissions, we selected seven high-quality articles that provide important contributions for curbing the spread of misinformation, manipulation, and abuse on social media. In the following, we briefly summarize each of the accepted articles.

The COVID-19 pandemic has been plagued by the pervasive spread of a large number of rumors and conspiracy theories, which even led to dramatic real-world consequences. “Conspiracy in the Time of Corona: Automatic Detection of Emerging COVID-19 Conspiracy Theories in Social Media and the News” by Shahsavari, Holur, Wang, Tangherlini, and Roychowdhury grounds on a machine learning approach to automatically discover and investigate the narrative frameworks supporting such rumors and conspiracy theories [ 17 ]. Authors uncover how the various narrative frameworks rely on the alignment of otherwise disparate domains of knowledge, and how they attach to the broader reporting on the pandemic. These alignments and attachments are useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Moreover, identifying the narrative frameworks that provide the generative basis for these stories may also contribute to devise methods for disrupting their spread.

The widespread diffusion of rumors and conspiracy theories during the outbreak has also been analyzed in “Partisan Public Health: How Does Political Ideology Influence Support for COVID-19 Related Misinformation?” by Nicholas Havey. The author investigates how political leaning influences the participation in the discourse of six COVID-19 misinformation narratives: 5G activating the virus, Bill Gates using the virus to implement a global surveillance project, the “Deep State” causing the virus, bleach, and other disinfectants as ingestible protection against the virus, hydroxychloroquine being a valid treatment for the virus, and the Chinese Communist party intentionally creating the virus [ 13 ]. Results show that conservative users dominated most of these discussions and pushed diverse conspiracy theories. The study further highlights how political and informational polarization might affect the adherence to health recommendations and can, thus, have dire consequences for public health.

Network based on the web-page URLs shared on Twitter from January 16, 2020 to April 15, 2020 [ 18 ]. Each node represents a web-page URL, while connections indicate links among web-pages. The purple nodes represent traditional news sources, the orange nodes indicate the low-quality and misinformation news sources, and the green nodes represent authoritative health sources. The edges take the color of the source, while the node size is based on the degree

“Understanding High and Low Quality URL Sharing on COVID-19 Twitter Streams” by Singh, Bode, Budak, Kawintiranon, Padden, and Vraga investigate URL sharing patterns during the pandemic, for different categories of websites [ 18 ]. Specifically, authors categorize URLs as either related to traditional news outlets, authoritative health sources, or low-quality and misinformation news sources. Then, they build networks of shared URLs (see Fig. 1 ). They find that both authoritative health sources and low-quality/misinformation ones are shared much less than traditional news sources. However, COVID-19 misinformation is shared at a higher rate than news from authoritative health sources. Moreover, the COVID-19 misinformation network appears to be dense (i.e., tightly connected) and disassortative. These results can pave the way for future intervention strategies aimed at fragmenting networks responsible for the spread of misinformation.

The relationship between news sentiment and real-world events is a long-studied matter that has serious repercussions for agenda setting and (mis-)information spreading. In “Around the world in 60 days: An exploratory study of impact of COVID-19 on online global news sentiment” , Chakraborty and Bose explore this relationship for a large set of worldwide news articles published during the COVID-19 pandemic [ 5 ]. They apply unsupervised and transfer learning-based sentiment analysis techniques and they explore correlations between news sentiment scores and the global and local numbers of infected people and deaths. Specific case studies are also conducted for countries, such as China, the US, Italy, and India. Results of the study contribute to identify the key drivers for negative news sentiment during an infodemic, as well as the communication strategies that were used to curb negative sentiment.

Farrell, Gorrell, and Bontcheva investigate one of the most damaging sides of online malicious content: online abuse and hate speech. In “Vindication, Virtue and Vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic” , they adopt a mixed methods approach to analyze citizen engagement towards British MPs online communications during the pandemic [ 11 ]. Among their findings is that certain pressing topics, such as financial concerns, attract the highest levels of engagement, although not necessarily negative. Instead, other topics such as criticism of authorities and subjects like racism and inequality tend to attract higher levels of abuse, depending on factors such as ideology, authority, and affect.

Yet, another aspect of online manipulation—that is, automation and social bot interference—is tackled by Uyheng and Carley in their article “Bots and online hate during the COVID-19 pandemic: Case studies in the United States and the Philippines”  [ 22 ]. Using a combination of machine learning and network science, the authors investigate the interplay between the use of social media automation and the spread of hateful messages. They find that the use of social bots yields more results when targeting dense and isolated communities. While the majority of extant literature frames hate speech as a linguistic phenomenon and, similarly, social bots as an algorithmic one, Uyheng and Carley adopt a more holistic approach by proposing a unified framework that accounts for disinformation, automation, and hate speech as interlinked processes, generating insights by examining their interplay. The study also reflects on the value of taking a global approach to computational social science, particularly in the context of a worldwide pandemic and infodemic, with its universal yet also distinct and unequal impacts on societies.

It has now become clear that text is not the only way to convey online misinformation and propaganda [ 10 ]. Instead, images such as those used for memes are being increasingly weaponized for this purpose. Based on this evidence, Wang, Lee, Wu, and Shen investigate US-targeted Chinese COVID propaganda, which happens to rely heavily on text images [ 23 ]. In their article “Influencing Overseas Chinese by Tweets: Text-Images as the Key Tactic of Chinese Propaganda” , they tracked thousands of Twitter accounts involved in the #USAVirus propaganda campaign. A large percentage ( \(\simeq 38\%\) ) of those accounts was later suspended by Twitter, as part of their efforts for contrasting information operations. Footnote 1 Authors studied the behavior and content production of suspended accounts. They also experimented with different statistical and machine learning models for understanding which account characteristics mostly determined their suspension by Twitter, finding that the repeated use of text images played a crucial part.

Overall, the great interest around the COVID-19 infodemic and, more broadly, about research themes such as online manipulation, automation, and abuse, combined with the growing risks of future infodemics, make this special issue a timely endeavor that will contribute to the future development of this crucial area. Given the recent advances and breadth of the topic, as well as the level of interest in related events that followed this special issue—such as dedicated panels, webinars, conferences, workshops, and other special issues in journals—we are confident that the articles selected in this collection will be both highly informative and thought provoking for readers. The diversity of the methodological and scientific approaches undertaken in the aforementioned articles demonstrates the interdisciplinarity of these issues, which demand renewed and joint efforts from different computer science fields, as well as from other related disciplines such as the social, political, and psychological sciences. To this regard, the articles in this collection testify and anticipate a growing trend of interdisciplinary studies where diverse theories, models, and techniques will be combined to tackle the different aspects at the core of online misinformation, manipulation, and abuse.

https://blog.twitter.com/en_us/topics/company/2020/information-operations-june-2020.html .

Alqurashi, S., Alhindi, A., & Alanazi, E. (2020). Large Arabic Twitter dataset on COVID-19. arXiv preprint arXiv:2004.04315 .

Banda, J.M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., & Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research—An international collaboration. arXiv preprint arXiv:2004.03688 .

Boneh, D., Grotto, A. J., McDaniel, P., & Papernot, N. (2019). How relevant is the Turing test in the age of sophisbots? IEEE Security & Privacy, 17 (6), 64–71.

Article   Google Scholar  

Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., et al. (2018). Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. American Journal of Public Health, 108 (10), 1378–1384.

Chakraborty, A., & Bose, S. (2020). Around the world in sixty days: An exploratory study of impact of COVID-19 on online global news sentiment. Journal of Computational Social Science .

Chen, E., Lerman, K., & Ferrara, E. (2020). Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set. JMIR Public Health and Surveillance, 6 (2), e19273.

Ciampaglia, G. L. (2018). Fighting fake news: A role for computational social science in the fight against digital misinformation. Journal of Computational Social Science, 1 (1), 147–153.

Cinelli, M., Cresci, S., Galeazzi, A., Quattrociocchi, W., & Tesconi, M. (2020). The limited reach of fake news on Twitter during 2019 European elections. PLoS One, 15 (6), e0234689.

Cresci, S. (2020). A decade of social bot detection. Communications of the ACM, 63 (10), 61–72.

Da San M., G., Cresci, S., Barrón-Cedeño, A., Yu, S., Di Pietro, R., & Nakov, P. (2020). A survey on computational propaganda detection. In: The 29th International Joint Conference on Artificial Intelligence (IJCAI’20), pp. 4826–4832.

Farrell, T., Gorrell, G., & Bontcheva, K. (2020). Vindication, virtue and vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 Pandemic. Journal of Computational Social Science .

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59 (7), 96–104.

Havey, N. (2020). Partisan public health: How does political ideology influence support for COVID-19 related misinformation?. Journal of Computational Social Science .

Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., et al. (2018). The science of fake news. Science, 359 (6380), 1094–1096.

Luceri, L., Deb, A., Giordano, S., & Ferrara, E. (2019). Evolution of bot and human behavior during elections. First Monday, 24 , 9.

Google Scholar  

Qazi, U., Imran, M., & Ofli, F. (2020). GeoCoV19: a dataset of hundreds of millions of multilingual COVID-19 tweets with location information. ACM SIGSPATIAL Special, 12 (1), 6–15.

Shahsavari, S., Holur, P., Wang, T., Tangherlini, T. R., & Roychowdhury, V. (2020). Conspiracy in the time of corona: Automatic detection of emerging COVID-19 conspiracy theories in social media and the news. Journal of Computational Social Science .

Singh, L., Bode, L., Budak, C., Kawintiranon, K., Padden, C., & Vraga, E. (2020). Understanding high and low quality URL sharing on COVID-19 Twitter streams. Journal of Computational Social Science .

Starbird, K. (2019). Disinformation’s spread: Bots, trolls and all of us. Nature, 571 (7766), 449–450.

Starbird, K., Dailey, D., Mohamed, O., Lee, G., & Spiro, E.S. (2018). Engage early, correct more: How journalists participate in false rumors online during crisis events. In: Proceedings of the 2018 ACM CHI Conference on Human Factors in Computing Systems (CHI’18), pp. 1–12. ACM.

Swire-Thompson, B., & Lazer, D. (2020). Public health and online misinformation: challenges and recommendations. Annual Review of Public Health, 41 , 433–451.

Uyheng, J., & Carley, K. M. (2020). Bots and online hate during the COVID-19 pandemic: Case studies in the United States and the Philippines. Journal of Computational Social Science .

Wang, A. H. E., Lee, M. C., Wu, M. H., & Shen, P. (2020). Influencing overseas Chinese by tweets: Text-Images as the key tactic of Chinese propaganda. Journal of Computational Social Science .

Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395 (10225), 676.

Download references

Author information

Authors and affiliations.

University of Southern California, Los Angeles, CA, 90007, USA

Emilio Ferrara

Institute of Informatics and Telematics, National Research Council (IIT-CNR), 56124, Pisa, Italy

Stefano Cresci

University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Manno, Switzerland

Luca Luceri

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Emilio Ferrara .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Ferrara, E., Cresci, S. & Luceri, L. Misinformation, manipulation, and abuse on social media in the era of COVID-19. J Comput Soc Sc 3 , 271–277 (2020). https://doi.org/10.1007/s42001-020-00094-5

Download citation

Received : 19 October 2020

Accepted : 23 October 2020

Published : 22 November 2020

Issue Date : November 2020

DOI : https://doi.org/10.1007/s42001-020-00094-5

Share this article

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

• Misinformation
• Social bots
• Social media
• Find a journal
• Publish with us
• Track your research

Essay on Social Media for School Students and Children

500+ words essay on social media.

Social media is a tool that is becoming quite popular these days because of its user-friendly features. Social media platforms like Facebook, Instagram, Twitter and more are giving people a chance to connect with each other across distances. In other words, the whole world is at our fingertips all thanks to social media. The youth is especially one of the most dominant users of social media. All this makes you wonder that something so powerful and with such a massive reach cannot be all good. Like how there are always two sides to a coin, the same goes for social media. Subsequently, different people have different opinions on this debatable topic. So, in this essay on Social Media, we will see the advantages and disadvantages of social media.

Advantages of Social Media

When we look at the positive aspect of social media, we find numerous advantages. The most important being a great device for education . All the information one requires is just a click away. Students can educate themselves on various topics using social media.

Moreover, live lectures are now possible because of social media. You can attend a lecture happening in America while sitting in India.

Furthermore, as more and more people are distancing themselves from newspapers, they are depending on social media for news. You are always updated on the latest happenings of the world through it. A person becomes more socially aware of the issues of the world.

In addition, it strengthens bonds with your loved ones. Distance is not a barrier anymore because of social media. For instance, you can easily communicate with your friends and relatives overseas.

Most importantly, it also provides a great platform for young budding artists to showcase their talent for free. You can get great opportunities for employment through social media too.

Another advantage definitely benefits companies who wish to promote their brands. Social media has become a hub for advertising and offers you great opportunities for connecting with the customer.

Get the huge list of more than 500 Essay Topics and Ideas

Disadvantages of Social Media

Despite having such unique advantages, social media is considered to be one of the most harmful elements of society. If the use of social media is not monitored, it can lead to grave consequences.

Thus, the sharing on social media especially by children must be monitored at all times. Next up is the addition of social media which is quite common amongst the youth.

This addiction hampers with the academic performance of a student as they waste their time on social media instead of studying. Social media also creates communal rifts. Fake news is spread with the use of it, which poisons the mind of peace-loving citizens.

In short, surely social media has both advantages and disadvantages. But, it all depends on the user at the end. The youth must particularly create a balance between their academic performances, physical activities, and social media. Excess use of anything is harmful and the same thing applies to social media. Therefore, we must strive to live a satisfying life with the right balance.

FAQs on Social Media

Q.1 Is social media beneficial? If yes, then how?

A.1 Social media is quite beneficial. Social Media offers information, news, educational material, a platform for talented youth and brands.

Q.2 What is a disadvantage of Social Media?

A.2 Social media invades your privacy. It makes you addicted and causes health problems. It also results in cyberbullying and scams as well as communal hatred.

Customize your course in 30 seconds

Which class are you in.

• Travelling Essay
• Picnic Essay
• Our Country Essay
• My Parents Essay
• Essay on Favourite Personality
• Essay on Memorable Day of My Life
• Essay on Knowledge is Power
• Essay on Gurpurab
• Essay on My Favourite Season
• Essay on Types of Sports

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

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
• Addict Behav Rep
• v.17; 2023 Jun

Social media use and abuse: Different profiles of users and their associations with addictive behaviours

Deon tullett-prado.

a Victoria University, Australia

Vasileios Stavropoulos

b University of Athens, Greece

Rapson Gomez

c Federation University, Australia

Associated Data

The data is made available via a link document.

Introduction

Social media use has become increasingly prevalent worldwide. Simultaneously, concerns surrounding social media abuse/problematic use, which resembles behavioural and substance addictions, have proliferated. This has prompted the introduction of ‘Social Media Addiction’ [SMA], as a condition requiring clarifications regarding its definition, assessment and associations with other addictions. Thus, this study aimed to: (a) advance knowledge on the typology/structure of SMA symptoms experienced and: (b) explore the association of these typologies with addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

A sample of 968 [Mage = 29.5, SDage = 9.36, nmales = 622 (64.3 %), nfemales = 315, (32.5 %)] adults was surveyed regarding their SMA experiences, using the Bergen Social Media Addiction Scale (BSMAS). Their experiences of Gaming, Internet, Gambling, Alcohol, Cigarette, Drug, Sex, Shopping and Exercise addictions were additionally assessed, and latent profile analysis (LPA) was implemented.

Three distinct profiles were revealed, based on the severity of one’s SMA symptoms: ‘low’, ‘moderate’ and ‘high’ risk. Subsequent ANOVA analyses suggested that participants classified as ‘high’ risk indicated significantly higher behaviours related to internet, gambling, gaming, sex and in particular shopping addictions.

Conclusions

Results support SMA as a unitary construct, while they potentially challenge the distinction between technological and behavioural addictions. Findings also imply that the assessment of those presenting with SMA behaviours, as well as prevention and intervention targeting SMA at risk groups, should consider other comorbid addictions.

1. Introduction

Social media – a form of online communication in which users create profiles, generate and share content, while forming online social networks/communities ( Obar & Wildman, 2015 ), is quickly growing to become almost all consuming in the media landscape. Currently the number of daily social media users exceeds 53 % (∼4.5 billion users) of the global population, approaching 80 % among more developed nations ( Countrymeters, 2021 , DataReportal, 2021 ). Due to technological advancements, the rise of ‘digital natives’ (i.e. children and adolescents raised with and familiarised with digital technology) and coronavirus pandemic triggered lockdowns, the frequency and duration of social media usage has been steadily increasing as people compensate for a lack of face to face interaction or grow with Social Media as a normal part of their lives (i.e. ∼ 2 h and 27 min average daily; DataReportal, 2021 , Heffer et al., 2019 , Zhong et al., 2020 , Nguyen, 2021 ). Furthermore, social media is increasingly involved in various domains of life including education, economics and even politics, to the point where engagement with the economy and wider society almost necessitates its use, driving the continued proliferation of social media use ( Calderaro, 2018 , Nguyen, 2021 , Mabić et al., 2020 , Mourão and Kilgo, 2021 ). This societal shift towards increased social media use has had some positive benefits, serving to facilitate the creation and maintenance of social groups, increase access to opportunities for career advancement and created wide ranging and accessible education options for many users ( Calderaro, 2018 , Prinstein et al., 2020 , Bouchillon, 2020 , Nguyen, 2021 ). However, for a minority of users - roughly 5–10 % ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 ) – social media use has become excessive, to the point where it dominates one’s life, similarly to an addictive behaviour - a state known as 'problematic social media use' ( Sun & Zhang, 2020 ). For these users, social media is experienced as the single most important activity in one’s life, while compromising their other roles and obligations (e.g. family, romance, employment; Sun and Zhang, 2020 , Griffiths and Kuss, 2017 ). This is a situation associated with low mood/depression, the compromise of one’s identity, social comparison leading to anxiety and self-esteem issues, work, academic/career difficulties, compromised sleep schedules and physical health, and even social impairment leading to isolation ( Anderson et al., 2017 , Sun and Zhang, 2020 , Gorwa and Guilbeault, 2020 ).

1.1. Problematic social media engagement in the context of addictions

Problematic social media use is markedly similar to the experience of substance addiction, thus leading to problematic social media use being modelled by some as a behavioural addiction - social media addiction (SMA; Sun and Zhang, 2020 ). In brief, an addiction loosely refers to a state where an individual experiences a powerful craving to engage with a behaviour, and inability to control their related actions, such that it begins to negatively impact their life ( Starcevic, 2016 ). Although initially the term referred to substance addictions induced by psychotropic drugs (e.g., amphetamines), it later expanded to include behavioural addictions ( Chamberlain et al., 2016 ). These reflect a fixation and lack of control, similar to those experienced in the abuse of substances, related to one’s excessive/problematic behaviours ( Starcevic, 2016 ).

Indeed, behavioural addictions, such as gaming, gambling and (arguably) social media addiction (SMA) share many common features with substance related addictions ( Zarate et al., 2022 ). Their similarities extend beyond the core addiction manifestations of fixation, loss of control and negative life consequences ( Grant et al., 2010 , Bodor et al., 2016 , Martinac et al., 2019 , Zarate et al., 2022 ). For instance, it has been evidenced that common risk factors/mechanisms (e.g., low impulse control), behavioural patterns (e.g., chronic relapse; sudden “spontaneous” quitting), ages of onset (e.g., adolescence and young adulthood) and negative life consequences (e.g., financial and legal difficulties) are similar between the so-called behavioural addictions and formally diagnosed substance addictions ( Grant et al., 2010 ). Moreover, such commonalities often accommodate the concurrent experience of addictive presentations, and/or even the substitution/flow from one addiction to the next (e.g., gambling and alcoholism; Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ).

With these features in mind, SMA has been depicted as characterized by the following six symptoms; A deep preoccupation with social media use (salience), use to either increase their positive feelings and/or buffer their negative feelings (mood modification), the requirement for progressively increasing time-engagement to get the same effect (i.e., tolerance), withdrawal symptoms such as irritability and frustration when access is reduced (withdrawal), the development of tensions with other people due to under-performance across several life domains (conflict) and reduced self-regulation resulting in an inability to reduce use (relapse; Andreassen et al., 2012 , Brown, 1993 , Griffiths and Kuss, 2017 , Sun and Zhang, 2020 ).

This developing model of SMA has been gaining popularity as the most widely used conceptualisation of problematic social media use, and guiding the development of relevant measurement tools ( Andreassen et al., 2012 , Haand and Shuwang, 2020 , Prinstein et al., 2020 ; Van den Eijnden et al., 2016) ). However, SMA is not currently uniformly accepted as an understanding of problematic social media use. Some critics have labelled the SMA model a premature pathologisation of ordinary social media use behaviours with low construct validity and little evidence for its existence, often inviting alternative proposed classifications derived by cognitive-behavioural or contextual models ( Sun & Zhang, 2020 ; Panova & Carbonell, 2018 7; Moretta, Buodo, Demetrovics & Potenza, 2022 ). Furthermore, the causes, risk factors and consequences of SMA, as well as the measures employed in its assessment have yet to be elucidated in depth, with research in the area being largely exploratory in nature ( Prinstein et al., 2020 , Sun and Zhang, 2020 ). In this context, what functional, regular and excessive social media use behaviours may involve has also been debated ( Wegmann et al., 2022 ). Thus, there is a need for further research clarifying the nature of SMA, identifying risk factors and related negative outcomes, as well as potential methods of treatment ( Prinstein et al., 2020 , Sun and Zhang, 2020 , Moretta et al., 2022 ).

Two avenues important for realizing these goals (and the focus of this study) involve: a) profiling SMA behaviours in the broader community, and b) decoding their associations with other addictions. Profiling these behaviours would involve identifying groups of people with particular patterns of use rather than simply examining trends in behaviour across the greater population. This would allow for clearer understandings of the ways in which different groups experience SMA and a more person-centred analysis (i.e., focused on finer understandings of personal experiences, Bányai et al., 2017 ). Moreover, when combined with analyses of association, it can allow for assertions not only about whether SMA associates with a variable, but about which components of the experience of SMA associate with a variable, allowing for more nuanced understandings. One such association with much potential for exploration, is that of SMA with other addictions (i.e., how does a certain SMA type differentially relate with other addictive behaviors, such as gambling and/or substance abuse?). Such knowledge would be useful, due to the shared common features and risk factors between addictions. It would allow for a greater understanding of the likelihood of comorbid addictions, or of flow from one addiction to the next ( Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ). However, the various links between different addictions are not identical, with alcoholism (for example) associating less strongly with excessive/problematic internet use than with problematic/excessive (so called “addictive) sex behaviours ( Grant et al., 2010 ). In that line, some studies have suggested the consideration of different addiction subgroups (e.g., substance, behavioural and technology addictions Marmet et al., 2019 ), and/or different profiles of individuals being prone to manifest some addictive behaviours more than others ( Zilberman et al., 2018 ). Accordingly, one may assume that distinct profiles of those suffering from SMA behaviours may be more at risk for certain addictions over others, rather than with addictions in general ( Zarate et al., 2022 ).

Understanding these varying connections could be vital for SMA treatment. Co-occurring addictions often reinforce each-other through their behavioural effects. Furthermore, by targeting only a single addiction type in a treatment, other addictions an individual is vulnerable to can come to the fore ( Grant et al., 2010 , Miller et al., 2019 ). Thus, a holistic view of addictive vulnerability may require consideration ( Grant et al., 2010 , Miller et al., 2019 ). This makes the identification of individual SMA profiles, as well as any potential co-occurring addictions, pivotal for more efficient assessment, prevention and intervention of SMA behaviours.

To the best of the authors’ knowledge, four studies to date have attempted to explore SMA profiles. Three of those have been conducted predominantly with European adolescent samples, and varied in terms of the type and number of profiles detected ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ). The fourth was conducted with English speaking adults from the United Kingdom and the United States ( Cheng et al., 2022 ). Of extant studies, Bányai et al. (2017) identified three profiles varying quantitively (i.e., in terms of their SMA symptoms’ severity) across a low, moderate and high range. In contrast, Brailovskaia et al., 2021 , Luo et al., 2021 identified four and five profiles that varied both quantitatively and qualitatively in terms of the type of SMA symptoms reported. Brailovskaia et al., (2021) proposed the ‘low symptom’, ‘low withdrawal’ (i.e., lower overall SMA symptoms with distinctively lower withdrawal), ‘high withdrawal’ (i.e., higher overall SMA symptoms with distinctively higher withdrawal) and ‘high symptom’ profiles. Luo et al. (2021) supported the ‘casual’, ‘regular’, ‘low risk high engagement’, ‘at risk high engagement’ and ‘addicted’ user profiles, which demonstrated progressively higher SMA symptoms severity alongside significant differences regarding mood modification, relapse, withdrawal and conflict symptoms, that distinguished the low and high risk ‘high engagement’ profiles. Finally, considering the occurrence of different SMA profiles in adults, Cheng and colleagues, (2022), supported the occurrence of ‘no-risk’, ‘at risk’ and ‘high risk’ social media users applying in both US and UK populations, with the UK sample showing a lower proportion of the ‘no-risk’ profile (i.e. UK = 55 % vs US = 62.2) and a higher percentage of the high risk profile (i.e. UK = 11.9 % vs US = 9.1 %). Thus, considering the number of identified profiles best describing the population of social media users, Cheng and colleagues’ findings (2022) were similar to Bányai and colleagues’ (2017) suggestions for SMA behaviour profiles of adolescents. At this point it should be noted, that none of the four studies exploring SMA behaviours profiles to date has taken into consideration different profile parameterizations, meaning that potential differences in the heterogeneity/ variability of those classified within the same profile were not considered (e.g. some profiles maybe more loose/ inclusive than others; Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ).

The lack of convergence regarding the optimum number and the description of SMA profiles occurring, as well as age, cultural and parameterization limitations of the four available SMA profiling studies, invites further investigation. This is especially evident in light of preliminary evidence confirming one’s SMA profile may link more to certain addictions over others ( Zarate et al., 2022 ). Indeed, those suffering from SMA behaviours have been shown to display heightened degrees of alcohol and drug use, a vulnerability to internet addiction in general, while presenting lower proneness towards exercise addiction and tobacco use ( Grant et al., 2010 , Anderson et al., 2017 , Duradoni et al., 2020 , Spilkova et al., 2017 ). In terms of gambling addiction, social media addicts display similar results on tests of value-based decision making as gambling addicts ( Meshi et al., 2019 ). Finally, regarding shopping addiction, the proliferation of advertisements for products online, and the ease of access via social media to online stores could be assumed to have an intensifying SMA effect ( Rose & Dhandayudham, 2014 ). Aside from these promising, yet relatively limited findings, the assessed connections between SMA and other addictions tend to be either addressed in isolation (e.g., SMA with gambling only and not multiple other addiction forms; Gainsbury et al., 2016a , Gainsbury et al., 2016b ) and in a variable (and not person) focused manner (e.g., higher levels of SMA relate with higher levels of drug addiction; Spilkova et al., 2017 ), which overlooks an individual’s profile. These profiles are vitally needed, as knowing the type of individual who may experience a series of disparate addictions is paramount for identifying at risk social media users and populations in need of more focused prevention/intervention programs ( Grant et al., 2010 ). Hence, using person focused methods such as latent profile(s) analysis (LPA) that address the ways in which distinct variations/profiles in SMA behaviours may occur, and how these relate with other addictions is imperative ( Lanza & Cooper, 2016 ).

1.2. Present study

To address this research priority, while considering SMA behaviours as being normally distributed (i.e., a minimum–maximum continuum) across the different profiles of users in the general population, the present Australian study uses a large community sample, solid psychometric measures and a sequence of differing in parameterizations LCA models aiming to: (a) advance past knowledge on the typology/structure of SMA symptom one experiences and: (b) innovatively explore the association of these typologies with a comprehensive list of addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

Based on Cheng and colleagues (2022) and Bányai and colleagues (2017), it was envisaged that three profiles arrayed in terms of ascending SMA symptoms’ severity would be likely identified. Furthermore, guided by past literature supporting closer associations between technological and behavioural addictions than with substance related addictions, it was hypothesized that those classified at higher SMA risk profiles would report higher symptoms of other technological and behavioural addictions, such as those related to excessive gaming and gambling, than with drug addiction ( Chamberlain and Grant, 2019 , Zarate et al., 2022 ).

2.1. Participants

The current study was conducted in Australia. Responses initially retrieved included 1097 participants. Of those, 129 were not considered for the current analyses. In particular, 84 respondents were classified as preview-only registrations and did not address any items, 5 presented with systematic response inconsistencies, and thus were considered invalid, 11 were excluded as potential bots, 11 had not provided their informed consent (i.e., did not tick the digital consent box, although they later addressed the survey), and 18 were taken out for not fulfilling age conditions (i.e., being adults), in line with the ethics approval received. Therefore, responses from 968 English-speaking adults from the general community were examined. An online sample of adult, English speaking participants aged 18 to 64 who were familiar with gaming [ N  = 968, M age  = 29.5, SD age  = 9.36, n males  = 622 (64.3 %), n females  = 315, (32.5 %), n trans/non-binary  = 26 (2.7 %), n queer  =  1 (0.1 %), n other  =  1 (0.1 %), n missing  =  3 (0.3 %)] was analysed. According to Hill (1998) random sampling error is required to lie below 4 %, that is satisfied by the current sample’s 3 % (SPH analytics, 2021). See Table 1 for participants’ sociodemographic information.

Socio-demographic and online use characteristics of participants.

Note: Percentages represent the percentage of that sex which is represented by any one grouping, rather than percentages of the overall population.

2.2. Measures

Psychometric instruments targeting sociodemographics, SMA and a semi-comprehensive range of behavioral, digital and substance addictions were employed. These instruments involved the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2012 ), the Internet Gaming Disorder 9 items Short Form (IGDS-SF9; Pontes & Griffiths, 2015 ), The Internet Disorder Scale (IDS9-SF; ( Pontes & Griffiths, 2016 ), the Online Gambling Disorder Questionnaire (IGD-Q; González-Cabrera et al., 2020 ), the 10-Item Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993 , the Five Item Cigarette Dependance Scale (CDS-5; Etter et al., 2003 ), the 10- item Drug Abuse Screening Test (DAST-10; Skinner, 1982 ), the Bergen-Yale Sex Addiction Scale (BYSAS; Andreassen et al., 2018), the Bergen Shopping Addiction Scale (BSAS; Andreassen et al., 2015) and the 6-item Revised Exercise Addiction Inventory (EAI-R; Szabo et al., 2019 ). Precise details of these measures, including values related to assumptions can be found in Table 2 .

Measure descriptions and internal consistency.

Note Table 2 : Streiner’s (2003) guidelines are used when measuring internal reliability, with Cronbachs Alpha scores in the range of 0.60–0.69 labelled ‘acceptable’, ranges between 0.70 and 0.89 labelled ‘good’ and ranges between 0.90 and 1.00 labelled ‘excellent’. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 ( Brown, 2006 ). OGD-G kurtosis (13.90) and skewness (3.45) exceeded the recommended limits ( Brown, 2006 ). However, LPA does not assume data distribution linearity, normality and or homogeneity ( Rosenberg et al., 2019 ). Considering aim B, related to detecting significant reported differences on measures for gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively, anova results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong, Saminathan, & Chang, 2016 ).

2.3. Procedure

Approval was received from the Victoria University Human Research Ethics Committee (HRE20-169). Data was collected in August 2019 to August 2020 via an online survey link distributed via social media (i.e., Facebook; Instagram; Twitter), digital forums (i.e. reddit) and the Victoria University learning management system. Familiarity with gaming was preferred, so that associations with one’s online gaming patterns were studied. The link first took potential participants to the Plain Language Information Statement (PLIS) which informed on the study requirements and participants’ anonymity and free of penalty withdrawal rights. Digital provision of informed consent (i.e., ticking a box) was required by the participants before proceeding to the survey.

2.4. Statistical analyses

Statistical analyses were conducted via: a) R-studio for the latent profile(s) analyses (LPA) and; b) Jamovi for descriptive statistics and profiles’ comparisons. Regarding aim A, LPA identified naturally homogenous subgroups within a population ( Rosenberg et al., 2019 ). Through the TIDYLPA CRAN R package, a number of models varying in terms of their structure/parameterization and the number of ‘profiles’ were tested using the six BSMAS criteria/items as indicators ( Rosenberg et al., 2019 ; see Table 3 ).

LCA model parameterization characteristics.

Subsequently, the constructed models were compared regarding selected fit indices (i.e., Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), bootstrapped Lo-Mendel Rubin test (B-LMR or LRT), entropy and the N_Min; Rosenberg et al., 2019 ) 1 . This involved 1: Dismissing any models with N -Min’s equalling 0, as each profile requires at least one participant, 2: Dismissing models with entropy scores below 0.64 ( Tein et al., 2013 ), 3: Dismissing models with nonsignificant BLMR value, and 4: assessing the remaining models on their AIC/BIC looking for an elbow point in the decline or the lowest values.

Regarding aim B of the study, ANOVA with bootstrapping (1000x) was employed to detect significant profile differences regarding one’s gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively.

All analyses’ assumptions were met with one exception 2 . The measure of Online Gambling disorder experience violated guidelines for the acceptable departure from normality and homogeneity ( Kim, 2013 ). Given this violation, results regarding gambling addiction should be considered with some caution.

3.1. Aim A: LPA of BSMAS symptoms

The converged models’ fit, varying by number of profiles and parametrization is displayed in Table 4 , with the CIP parameterisation presenting as the optimum (i.e. lower AIC and BIC, and 1–8 profiles converging; all CVDP, CIUP, CVUP models did not converge except the CVUP one profile). Subsequently, the CIP models were further examined via the TIDYLPA Mclust function (see Table 5 ). AIC and BIC decreased as the number of profiles increased. This flattened past 3 profiles (i.e., elbow point; Rosenberg et al., 2019 ). Furthermore, past 3 profiles, N -min reached zero, indicating profiles with zero participants in them – thus reducing interpretability. Lastly, the BLRT test reached non significance once the model had 4 profiles, again indicating the 3-profile model as best fitting. Therefore, alternative CIP -models were rejected in favour of the 3-profile one. This displayed a level of classification accuracy well above the suggested cut off point of 0.76 (entropy = 0.90; Larose et al., 2016 ), suggesting over 90 % correct classification ( Larose et al., 2016 ). Regarding the profiles’ proportions, counts revealed 33.6 % as profile 1, 52.4 % as profile 2, 14 % as profile 3.

Initial model testing.

Fit indices of cip models with 1–8 classes.

Table 6 and Fig. 1 present the profiles’ raw mean scores across the 6 BSMAS items whilst Table 7 and Fig. 2 present the standardised mean scores.

Raw Mean Scores and Standard Error of the 6 BSMAS Criteria Across the Three Classes/Profiles.

Raw symptom experience of the three classes.

Standardised mean scores of the 6 bsmas criteria Across the Three Classes/Profiles.

Note: For standard errors, see Table 6 .

Standardized symptom experience of the three classes.

Profile 1 scores varied from 1.74 to 2.98 raw and between 0.08 and 0.58 standard deviations above the sample mean symptom experience. In terms of plateaus and steeps, profile 1 displayed a raw score plateaus across symptoms 1–3 (salience, tolerance, mood modification), a decline in symptom 4 (relapse), and another plateau across symptoms 5–6 (withdrawal and conflict). It further displayed a standardized score plateau around the level of 0.5 standard deviations across symptoms 1–3 and a decline across symptoms 4–6. Profile 2 varied consistently between raw mean scores of 1 and 1.36 across the 6 SMA symptoms, and between −0.74 and −0.53 standard deviations from the sample mean with general plateaus in standardized score across symptoms 1–3 and 4–6. Finally, profile 3 mean scores varied between 3.02 and 3.95 raw and 1.26 to 1.88 standardized. Plateaus were witnessed in the raw scores across symptoms 1–3 (salience, tolerance, mood modification), a decline at symptom 4 (relapse), a relative peak at symptom 5 (withdrawal), and a further decline across symptom 6 (conflict). However, the standardized scores for profile 3 were relatively constant across the first four symptoms, before sharply reaching a peak at symptom 5 and then declining once more. Accordingly, the three profiles were identified as severity profiles ‘Low’ (profile 2), ‘Moderate’ (profile 1) and ‘High’ (profile 3) risk. Table 8 , Table 9 provide the profile means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed.

Post Hoc Descriptives across a semi-comprehensive list of addictions.

Post Hoc Comparisons of the SMA profiles revealed across the addictive behaviors measured.

3.2. Aim 2: BSMAS profiles and addiction risk/personal factors.

Table 8 , Table 9 display the Jamovi outputs for the BSMAS profiles and their means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed using ANOVA. Cohen’s (1988) benchmarks were used for eta squared values, with > 0.01 indicating small, >0.059 medium and > 0.138 large effects. ANOVA results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong et al., 2016 ). SMA profiles significantly differed across the range of behavioral addiction forms examined with more severe SMA profiles presenting consistently higher scores with a medium effect size regarding gaming ( F  = 57.5, p  <.001, η 2  = 0.108), sex ( F  = 39.53, p  <.001, η 2  = 0.076) and gambling ( F  = 40.332, p  <.001, η 2  = 0.078), and large effect sizes regarding shopping ( F  = 90.06, p  <.001, η 2  = 0.159) and general internet addiction symptoms ( F  = 137.17, p  <.001, η2 = 0.223). Only relationships of ‘medium’ size or greater were considered further in this analysis, though small effects were found with alcoholism ( F  = 11.34, p  <.001, η 2  = 0.023), substance abuse ( F  = 4.83, p  =.008, η 2  = 0.01) and exercise addiction ( F  = 5.415, p  =.005, η2 = 0.011). Pairwise comparisons consistently confirmed that the ‘low’ SMA profile scored significantly lower than the ‘moderate’ and the ‘high’ SMA profile’, and the ‘moderate’ SMA profile being significantly lower than the ‘high’ SMA profile across all addiction forms assessed (see Table 8 , Table 9 ).

4. Discussion

The present study examined the occurrence of distinct SMA profiles and their associations with a range of other addictive behaviors. It did so via uniquely combining a large community sample, measures of established psychometric properties addressing both SMA and an extensive range of other proposed substance and behavioral addictions, to calculate the best fitting model in terms of parameterization and profile number. A model of the CIP parameterization with three profiles was supported by the data. The three identified SMA profiles ranged in terms of severity and were labeled as ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk. Membership of the ‘high’ SMA risk profile was shown to link with significantly higher reported experiences of Internet and shopping addictive behaviours, and moderately with higher levels of addictive symptoms related to gaming, sex and gambling.

4.1. Number and variations of SMA profiles

Three SMA profiles, entailing ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’(14 %) SMA risk were supported, with symptom 5 – withdrawal – displaying the highest inter-profile disparities. These results help clarify the number of SMA profiles in the population, as past findings were inconsistent supporting either 3, or 4 or 5 SMA profiles ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 ), as well as the nature of the differences between these profiles (i.e. quantitative: “how much/high one experiences SMA symptoms” or qualitative: “the type of SMA symptoms one experiences”). Our findings are consistent with the findings of Bányai and colleagues (2017) and Cheng and colleagues (2022) indicating a unidimensional experience of SMA (i.e., that the intensity/severity an individual reports best defines their profile membership, rather than the type of SMA symptoms) with three profiles ranging in severity from ‘low’ to ‘moderate’ to ‘high’ and those belonging at the higher risk profiles being the minority. Conversely, these results stand in opposition with two past studies identifying profiles that varied qualitatively (i.e., specific SMA symptoms experienced more by certain profiles) and suggesting the occurrence of 4 and 5 profiles respectively ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Such differences might be explained by variations in the targeted populations of these studies. Characteristics such as gender, nationality and age all have significant effects on how and why social media is employed ( Andreassen et al., 2016 ; Hsu et al., 2015 ; Park et al., 2015 ). Given that the two studies in question utilized European, adolescent samples, the difference in the culture and age of our samples may have produced our varying results, ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Comparability issues may also explain these results, given the profiling analyses implemented in the studies of Brailovskaia and colleagues, (2021), as well as Luo and colleagues (2021) did not extensively consider different profiles parameterizations, as the present study and Cheng et al. (2022) did. Furthermore, the results of this study closely replicated those of the Cheng et al., (2022) study, with both studies identifying a near identical pattern of symptom experience across three advancing levels of severity. This replication of results may indicate their accuracy, strengthening the validity of SMA experience models involving 3 differentiated profiles of staggered severity. Both our findings and Cheng et al.’s findings indicate profiles characterized by higher levels of cognitive symptoms (salience, withdrawal and mood modification) for each class when compared to their experience of behavioral symptoms (Relapse, withdrawal, conflict; Cheng et al., 2022 ). Further research may focus on any potentially mediating/moderating factors that may be interfering, and potentially further replicate such results, proving their reliability. Furthermore, given that past studies (with different results) utilized European, adolescent samples, cultural and age comparability limitations need to be considered and accounted for in future research ( Bányai et al., 2017 , Brailovskaia et al., 2021 ; Cheng et al., 2022 ).

Regarding withdrawal being the symptom of highest discrepancy between profiles, findings suggest that it may be more SMA predictive, and thus merit specific assessment or diagnostic attention, aligning with past literature ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 , Smith and Short, 2022 ). Indeed, the experience of irritability and frustration when abstaining from usage has been shown to possess higher differentiation power regarding diagnosing and measuring other technological addictions such as gaming, indicating the possibility of a broader centrality to withdrawal across the constellation of digital addictions ( Gomez et al., 2019 ; Schivinski et al., 2018 ).

Finally, the higher SMA risk profile percentage in the current study compared with previous research [e.g., 14 % in contrast to the 4.5 % ( Bányai et al., 2017 ), 4.2 % ( Luo et al., 2021 ) and 7.2 % ( Brailovskaia et al., 2021 )] also invites significant plausible interpretations. The data collection for the present Australian study occurred between August 2019 to August 2020, while Bányai and their colleagues (2017) collected their data in Hungary in March 2015, and Brailovskaia and their colleagues (2021) in Lithuania and Germany between October 2019 and December 2019. The first cases of the COVID-19 pandemic outside China were reported in January 2020, and the pandemic isolation measures prompted more intense social media usage, to compensate for their lack of in person interactions started unfolding later in 2020 ( Ryan, 2021 , Saud et al., 2020 ). Thus, it is likely that the higher SMA symptom scores reported in the present study are inflated by the social isolation conditions imposed during the time the data was collected. Furthermore, the present study involves an adult English-speaking population rather than European adolescents, as the studies of Bányai and their colleagues (2017) and Brailovskaia and their colleagues (2021). Thus, age and/or cultural differences may explain the higher proportion of the high SMA risk profile found. For instance, it is possible that there may be greater SMA vulnerability among older demographics and/or across countries. The explanation of differences across counties is reinforced by the findings of Cheng and colleagues (2022) who assessed and compared UK and US adult populations, the first is less likely, as younger age has been shown to relate to higher SMA behaviors ( Lyvers et al., 2019 ). Overall, the present results closely align with that of Cheng and colleagues (2022), who also collected their data during a similar period (between May 18, 2020 and May 24, 2020) from English speaking countries (as the present study did). They, in line with our findings, also supported the occurrence of three SMA behavior profiles, with the low risk profile exceeding 50 % of the general population and those at higher risk ranging above 9 %.

4.2. Concurrent addiction risk

Considering the second study aim, ascending risk profile membership was strongly related to increased experiences of internet and shopping addiction, while it moderately connected with gaming, gambling and sex addictions. Finally, it weakly associated with alcohol, exercise and drug addictions. These findings constitute the first semi-comprehensive cross-addiction risk ranking of SMA high-risk profiled individuals, allowing the following implications.

Firstly, no distinction was found between the so called “technological” and other behavioral addictions, potentially contradicting prior theory on the topic ( Gomez et al., 2022 ). Typically, the abuse of internet gaming/pornography/social media, has been classified as behavioral addiction ( Enrique, 2010 , Savci and Aysan, 2017 ). However, their shared active substance – the internet – has prompted some scholars to suggest that these should be classified as a distinct subtype of behavioral addictions named “technological/ Internet Use addictions/disorders” ( Savci & Aysan, 2017 ). Nevertheless, the stronger association revealed between the “high” SMA risk profile and shopping addictions (not always necessitating the internet), compared to other technology related addictions, challenges this conceptual distinction ( Savci & Aysan, 2017 ). This finding may point to an expanding intersection between shopping and SMA, as an increasing number of social media platforms host easily accessible product and services advertising channels (e.g., Facebook property and car selling/marketing groups, Instagram shopping; Rose & Dhandayudham, 2014 ). In turn, the desire to shop may prompt a desire to find these services online, share shopping endeavors with others or find deals one can only access through social media creating a reciprocal effect ( Rose & Dhandayudham, 2014 ). This possibility aligns with previous studies assuming reciprocal addictive co-occurrences ( Tullett-Prado et al., 2021 ). This relationship might also be exacerbated by shared causal factors underpinning addictions in general, such as one’s drive for immediate gratification and/or impulsive tendencies ( Andreassen et al., 2016 ; Niedermoser et al., 2021 ). Although such interpretations remain to be tested, the strong SMA and shopping addiction link evidenced suggests that clinicians should closely examine the shopping behaviors of those suffering from SMA behaviours, and if comorbidity is detected – address both addictions concurrently ( Grant et al., 2010 , Miller et al., 2019 ). Conclusively, despite some studies suggesting the distinction between technological, and especially internet related (e.g., SMA, internet gaming), addictions and other behavioral addictions ( Gomez et al., 2022 , Zarate et al., 2022 ), the current study’s high risk SMA profile associations appear not to differentiate based on the technological/internet nature that other addictions may involve.

Secondly, results suggest a novel hierarchical list of the types of addictions related to the higher SMA risk profile. While previous research has established links between various addictive behaviors and SMA (i.e., gaming and SMA; Wang et al., 2015 ), these have never before - to the best of the authors’ knowledge – been examined simultaneously allowing their comparison/ranking. Therefore, our findings may allow for more accurate predictions about the addictive comorbidities of SMA, aiding in SMA’s assessment and treatment. For example, Internet, shopping, gambling, gaming and sex addictions were all shown to more significantly associate with the high risk SMA profile than exercise and substance related addictive behaviors ( King et al., 2014 ; Gainsbury et al., 2016a ; Gainsbury et al., 2016b ; Rose and Dhandayudham, 2014 , Kamaruddin et al., 2018 , Leung, 2014 ). Thus, clinicians working with those with SMA may wish to screen for gaming and sex addictions. Regardless of the underlying causes, this hierarchy provides the likelihood of one addiction precipitating and perpetuating another in a cyclical manner, guiding assessment, prevention, and intervention priorities of concurrent addictions.

Lastly, these results indicate a lower relevance of the high risk SMA profile with exercise/substance addictive behaviors. Considering excessive exercise, our study reinforces literature indicating decreased physical activity among SMA and problematic internet users in general ( Anderson et al., 2017 , Duradoni et al., 2020 ). Naturally, those suffering from SMA behaviours spend large amounts of time sedentary in front of a screen, precluding excessive physical activities. Similarly, the lack of a significant relationship between tobacco abuse and SMA has also been identified priori, perhaps due to the cultural divide between social media and smoking in terms of their acceptance by wider society and of the difference in their users ( Spilkova et al., 2017 ). Contrary to expectations, there were weak/negligible associations between the high SMA risk profile with substance and alcohol abuse behaviours. This finding contradicts current knowledge supporting their frequent comorbidity ( Grant et al., 2010 , Spilkova et al., 2017 ; Winpenny et al., 2014 ). This finding may potentially be explained by individual differences between these users, as while one can assume many traits are shared between those vulnerable to substances and SMA, these may be expressed differently. For example, despite narcissism being a common addiction risk factor, its predictive power is mediated by reward sensitivity in SMA, where in alcoholism and substances, no such relationship exists ( Lyvers et al., 2019 ). Perhaps the constant dopamine rewards and the addictive reward schedule of social media targets this vulnerability in a way that alcoholism does not. Overall, one could assume that the associations between SMA and less “traditionally” (i.e., substance related; Gomez et al., 2022 ) viewed addictions deserves more attention. Thus, future research is recommended.

4.3. Limitations and future direction

The current findings need to be considered in the light of various limitations. Firstly, limitations related to the cross-sectional, age specific and self-report surveyed data are present. These methodological restrictions do not allow for conclusions regarding the longitudinal and/or causal associations between different addictions, nor for generalization of the findings to different age groups, such as adolescents. Furthermore, the self-report questionnaires employed may accommodate subjectivity biases (e.g., subjective and/or false memory recollections; Hoerger & Currell, 2012 ; Sun & Zhang, 2020 The latter risk is reinforced by the non-inclusion of social desirability subscales in the current study, posing obstacles in ensuring participant responses are accurate.

Additionally, there is a conceptual overlap between SMA and Internet Addiction (IA), which operates as an umbrella construct inclusive of all online addictions (i.e., irrespective of the aspect of the Internet being abused; Anderson et al., 2017 , Savci and Aysan, 2017 ). Thus, caution is warranted considering the interpretation of the SMA profiles and IA association, as SMA may constitute a specific subtype included under the IA umbrella ( Savci & Aysan, 2017 ). However, one should also consider that: (a) SMA, as a particular IA subtype is not identical to IA ( Pontes, & Griffiths, 2014 ); and (b) recent findings show that IA and addictive behaviours related to specific internet applications, such as SMA, could correlate with different types of electroencephalogram [EEG] activity, suggesting their neurophysiological distinction (e.g. gaming disorder patients experience raised delta and theta activity and reduced beta activity, while Internet addiction patients experience raised gamma and reduced beta and delta activity; Burleigh et al., 2020 ). Overall, these advocate in favour of a careful consideration of the SMA profiles and IA associations.

Finally, the role of demographic differences, related to one’s gender and age, which have been shown to mediate the relationship between social media engagement and symptoms of other psychiatric disorders ( Andreassen et al., 2016 ) have not been attended here.

Thus, regarding the present findings and their limitations, future studies should focus on a number of key avenues; (1) achieving a more granular understanding of SMA’s associations with comorbid addictions via case study or longitudinal research (e.g., cross lag designs), (2) further clarifying the nature of the experience of SMA symptoms, (3) investigating the link between shopping addiction and SMA, as well as potential interventions that target both of these addictions simultaneously and, (4) attending to gender and age differences related to the different SMA risk profiles, as well as how these may associate with other addictions.

5. Conclusion

The present study bears significant implications for the way that SMA behaviours are assessed among adults in the community and subsequently addressed in adult clinical populations. By profiling the ways in which SMA symptoms are experienced, three groups of adult social media users, differing regarding the reported intensity of their SMA symptoms were revealed. These included the ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk profiles. The high SMA risk profile membership was strongly related to increased rates of reported internet and shopping related addictive behaviours, moderately associated with gaming, gambling and sex related addictive behaviours and weakly associated with alcohol, exercise and drug related addictive behaviours, to the point that such associations were negligible at most. These results enable a better understanding of those experiencing higher SMA behaviours, and the introduction of a risk hierarchy of SMA-addiction comorbidities that needs to be taken into consideration when assessing and/or treating those suffering from SMA symptoms. Specifically, SMA and its potential addictive behaviour comorbidities may be addressed with psychoeducation and risk management techniques in the context of SMA relapse prevention and intervention plans, with a greater emphasis on shopping and general internet addictive behaviours. Regarding epidemiological implications, the inclusion of 14 % of the sample in the high SMA risk profile implies that while social media use can be a risky experience, it should not be over-pathologized. More importantly, and provided that the present findings are reinforced by other studies, SMA awareness campaigns might need to be introduced, while regulating policies should concurrently address the risk for multiple addictions among those suffering from SMA behaviours.

Note 1: Firstly, results were compared across all converged models. In brief, the AIC and BIC are measures of the prediction error which penalize goodness of fit by the number of parameters to prevent overfit, models with lower scores are deemed better fitting ( Tein et al., 2013 ). Of the 16 possible models, the parameterization with the most consistently low AIC’s and BIC’s across models with 1–8 profiles were chosen, eliminating 8 of the possible models. Subsequently, the remaining models were more closely examined through TIDYLPA using the compare solutions command, with the. BLMR operating as a direct comparison between 2 models (i.e. the model tested and a similar model with one profile less) on their relative fit using likelihood ratios. A BLMR based output p value will be obtained for each comparison pair with lower p-values corresponding to the greater fit among the models tested (i.e. if BLMR p >.05, the model with the higher number of profiles needs to be rejected; Tein et al., 2013). Entropy is an estimate of the probability that any one individual is correctly allocated in their profile/profile. Entropy ranges from 0 to 1 with higher scores corresponding with a better model ( Tein et al., 2013 ; Larose et al., 2016 ). Finally, the N_min represents the minimum proportion of sample participants in any one presentation profile and aids in determining the interpretability/parsimony of a model. If N_min is 0, then there is a profile or profilees in the model empty of members. Thus, the interpretability and parsimony of the model is reduced ( CRAN, 2021 ). These differing fit indices were weighed up against eachother in order to identify the best fitting model (Akogul & Erisoglu, 2017). This best fitting model was subsequently applied to the datasheet, and then the individual profilees examined through the use of descriptive statistics in order to identify their characteristics.

Note 2: With regards to the assumptions of the LPA Model, as a non-parametric test, no assumptions were made regarding the distribution of data. With regards to the subsequent ANOVA analyses, 2 assumptions were made as to the nature of the distribution. Homogeneity of variances and Normality. Thus, the distribution of the data was assessed via Jamovi. Skewness and Kurtosis for all measures employed in the ANOVA analyses. Skewness ranged from 0.673 to 2.49 for all variables bar the OGD-Q which had a skewness of 3.45. Kurtosis ranged from 0.11 to 6 for variables bar the OGD-Q which had a kurtosis of 13.9. Thus, all measures excepting the OGD-Q sat within the respective acceptable ranges of + 3 to −3 and + 10 to −10 recommended by Brown and Moore (2012).

Dr Vasileios Stavropoulos received funding by:

The Victoria University, Early Career Researcher Fund ECR 2020, number 68761601.

The Australian Research Council, Discovery Early Career Researcher Award, 2021, number DE210101107.

Ethical Standards – Animal Rights

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Thus, the present study was approved by the Human Ethics Research Committee of Victoria University (Australia).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Confirmation statement

Authors confirm that this paper has not been either previously published or submitted simultaneously for publication elsewhere.

Publication

Authors confirm that this paper is not under consideration for publication elsewhere. However, the authors do disclose that the paper has been considered elsewhere, advanced to the pre-print stage and then withdrawn.

Authors assign copyright or license the publication rights in the present article.

Availability of data and materials

Data is deposited as a supplementary file with the current document.

CRediT authorship contribution statement

Deon Tullett-Prado: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation. Vasileios Stavropoulos: Supervision, Resources, Funding acquisition, Project administration. Rapson Gomez: Supervision, Resources. Jo Doley: Supervision, Resources.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Biographies

Deon Tullett-Prado: Deon Tullett-Prado is a PhD candidate and emerging researcher in the area of behavioral addictions and in particular Internet Gaming Disorder. His expertise involves advanced statistical analysis skills and innovative techniques regarding population profiling.

Dr Vasileios Stavropoulos: Dr Vasileios Stavropoulos is a member of the Australian Psychological Society (APS) and a registered psychologist endorsed in Clinical Psychology with the Australian Health Practitioner Regulation Authority (AHPRA). Vasileios' research interests include the areas of Behavioral Addictions and Developmental Psychopathology. In that context, Vasileios is a member of the European Association of Developmental Psychology (EADP) and the EADP Early Researchers Union. Considering his academic collaborations, Vasileios maintains his research ties with the Athena Studies for Resilient Adaptation Research Team of the University of Athens, the International Gaming Centre of Nottingham Trent University, Palo Alto University and the Korean Advanced Institute of Science and Technology. Vasileios has received the ARC DECRA award 2021.

Dr Rapson Gomez: Rapson Gomez is professor in clinical psychology who once directed clinical training at the School of Psychology, University of Tasmania (Hobart, Australia). Now he focuses on research using innovative statistical techniques with a particular focus on ADHD, biological methods of personality, psychometrics and Cyberpsychology.

Dr Jo Doley: A lecturer at Victoria University, Dr Doley has a keen interest in the social aspects of body image and eating disorders. With expertise in a variety of quantitative methodologies, including experimental studies, delphi studies, and systematic reviews, Dr Doley has been conducting research into the ways that personal characteristics like sexual orientation and gender may impact on body image. Furthermore, in conjunction with the cyberpsychology group at VU they have been building a new expertise on digital media and it’s potential addictive effects.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.abrep.2023.100479 .

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Data availability

• Anderson E.L., Steen E., Stavropoulos V. Internet use and Problematic Internet Use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth. 2017; 22 (4):430–454. doi: 10.1080/02673843.2016.1227716. [ CrossRef ] [ Google Scholar ]
• Andreassen C.S., Torsheim T., Brunborg G.S., Pallesen S. Development of a Facebook addiction scale. Psychological reports. 2012; 110 (2):501–517. doi: 10.2466/02.09.18.PR0.110.2.501-517. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Andreassen C.S., Billieux J., Griffiths M.D., Kuss D.J., Demetrovics Z., Mazzoni E., et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors. 2016; 30 (2):252. doi: 10.1037/adb0000160. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bányai F., Zsila Á., Király O., Maraz A., Elekes Z., Griffiths M.D., et al. Problematic social media use: Results from a large-scale nationally representative adolescent sample. PloS one. 2017; 12 (1):e0169839. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bodor D., Tomić A., Ricijaš N., Filipčić I. Impulsiveness in alcohol addiction and pathological gambling. Alcoholism and psychiatry research: Journal on psychiatric research and addictions . 2016; 52 (2):149–158. doi: 10.20471/apr.2016.52.02.05. [ CrossRef ] [ Google Scholar ]
• Bouchillon B.C. Social networking for interpersonal life: A competence-based approach to the rich get richer hypothesis. Social Science Computer Review. 2020; 0894439320909506 doi: 10.1177/0894439320909506. [ CrossRef ] [ Google Scholar ]
• Brailovskaia J., Truskauskaite-Kuneviciene I., Kazlauskas E., Margraf J. The patterns of problematic social media use (SMU) and their relationship with online flow, life satisfaction, depression, anxiety and stress symptoms in Lithuania and in Germany. Current Psychology. 2021; 1–12 doi: 10.1007/s12144-021-01711-w. [ CrossRef ] [ Google Scholar ]
• Brown R.I.F. Some contributions of the study of gambling to the study of other addictions. Gambling behavior and problem gambling. 1993; 1 :241–272. [ Google Scholar ]
• Brown T.A. Confirmatory factor analysis for applied research. The Guilford Press; 2006. [ Google Scholar ]
• Burleigh T.L., Griffiths M.D., Sumich A., Wang G.Y., Kuss D.J. Gaming disorder and internet addiction: A systematic review of resting-state EEG studies. Addictive Behaviors. 2020; 107 (106429):32283445. doi: 10.1016/j.addbeh.2020.106429. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Calderaro A. In: Outhwaite W., Turner S., editors. Volume Set. SAGE Publications; 2018. Social media and politics. (The SAGE Handbook of Political Sociology: Two). [ CrossRef ] [ Google Scholar ]
• Chamberlain S.R., Grant J.E. In: A transdiagnostic approach to obsessions, compulsions and related phenomena. Cambridge. Fontenelle L.F., Yücel M., editors. University Press; 2019. Behavioral addictions. [ CrossRef ] [ Google Scholar ]
• Chamberlain S.R., Lochner C., Stein D.J., Goudriaan A.E., van Holst R.J., Zohar J., et al. Behavioural addiction—a rising tide? European neuropsychopharmacology. 2016; 26 (5):841–855. doi: 10.1016/j.euroneuro.2015.08.013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheng C., Ebrahimi O.V., Luk J.W. Heterogeneity of prevalence of social media addiction across multiple classification schemes: Latent profile analysis. Journal of Medical Internet Research. 2022; 24 (1):e27000. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Countrymeters. (2021). World Population. Retrieved from https://rb.gy/v6cqlq.
• CRAN. (2021). Introduction to tidyLPA. CRAN. https://cran.r-project.org/web/packages/tidyLPA/vignettes/Introduction_to_tidyLPA.html .
• DataReportal. (2021). DIGITAL 2021 OCTOBER GLOBAL STATSHOT REPORT. Retrieved from https://datareportal.com/reports/digital-2021-october-global-statshot.
• Duradoni M., Innocenti F., Guazzini A. Well-being and social media: A systematic review of Bergen addiction scales. Future Internet. 2020; 12 (2):24. doi: 10.3390/fi12020024. [ CrossRef ] [ Google Scholar ]
• Enrique E. Addiction to new technologies and to online social networking in young people: A new challenge. Adicciones. 2010; 22 (2) doi: 10.20882/adicciones.196. DOI. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Etter J.F., Le Houezec J., Perneger T. A Self-Administered Questionnaire to Measure Dependence on Cigarettes: The Cigarette Dependence Scale. Neuropsychopharmacol. 2003; 28 :359–370. doi: 10.1038/sj.npp.1300030. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grant J.E., Potenza M.N., Weinstein A., Gorelick D.A. Introduction to behavioral addictions. The American journal of drug and alcohol abuse. 2010; 36 (5):233–241. doi: 10.3109/00952990.2010.491884. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Griffiths M.D., Kuss D. Adolescent social media addiction (revisited) Education and Health. 2017; 35 (3):49–52. [ Google Scholar ]
• Gomez R., Stavropoulos V., Brown T., Griffiths M.D. Factor structure of ten psychoactive substance addictions and behavioural addictions. Psychiatry Research. 2022; 114605 doi: 10.1016/j.psychres.2022.114605. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gainsbury S.M., King D.L., Russell A.M., Delfabbro P., Derevensky J., Hing N. Exposure to and engagement with gambling marketing in social media: Reported impacts on moderate-risk and problem gamblers. Psychology of Addictive Behaviors. 2016; 30 (2):270. doi: 10.1037/adb0000156. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gainsbury S.M., Delfabbro P., King D.L., Hing N. An exploratory study of gambling operators’ use of social media and the latent messages conveyed. Journal of Gambling Studies. 2016; 32 (1):125–141. doi: 10.1007/s10899-015-9525-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gomez R., Stavropoulos V., Beard C., Pontes H.M. Item response theory analysis of the recoded internet gaming disorder scale-short-form (IGDS9-SF) International Journal of Mental Health and Addiction. 2019; 17 (4):859–879. doi: 10.1007/s11469-018-9890-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• González-Cabrera J., Machimbarrena J.M., Beranuy M., Pérez-Rodríguez P., Fernández-González L., Calvete E. Journal of Clinical Medicine . Vol. 9. MDPI AG; 2020. Design and Measurement Properties of the Online Gambling Disorder Questionnaire (OGD-Q) in Spanish Adolescents; p. 120. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gorwa R., Guilbeault D. Unpacking the social media bot: A typology to guide research and policy. Policy & Internet. 2020; 12 (2):225–248. doi: 10.1002/poi3.184. [ CrossRef ] [ Google Scholar ]
• Haand R., Shuwang Z. The relationship between social media addiction and depression: A quantitative study among university students in Khost, Afghanistan. International Journal of Adolescence and Youth. 2020; 25 (1):780–786. doi: 10.1080/02673843.2020.1741407. [ CrossRef ] [ Google Scholar ]
• Heffer, T., Good, M., Daly, O., MacDonell, E., & Willoughby, T. (2019). The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: An empirical reply to Twenge et al.(2018).  Clinical Psychological Science ,  7 (3), 462-470. DOI: https://doi.org/10.1177/216770261881272.
• Hoerger M., Currell C. In: APA handbook of ethics in psychology, Vol. 2. Practice, teaching, and research. Knapp S.J., Gottlieb M.C., Handelsman M.M., VandeCreek L.D., editors. American Psychological Association; 2012. Ethical issues in Internet research; pp. 385–400. [ CrossRef ] [ Google Scholar ]
• Hsu M.H., Chang C.M., Lin H.C., Lin Y.W. Determinants of continued use of social media: the perspectives of uses and gratifications theory and perceived interactivity. Information Research. 2015 [ Google Scholar ]
• Kamaruddin N., Rahman A.W.A., Handiyani D. Pornography addiction detection based on neurophysiological computational approach. Indonesian Journal of Electrical Engineering and Computer Science. 2018; 10 (1):138–145. [ Google Scholar ]
• Kim H.Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics. 2013; 38 (1):52–54. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• King D.L., Delfabbro P.H., Kaptsis D., Zwaans T. Adolescent simulated gambling via digital and social media: An emerging problem. Computers in Human Behavior. 2014; 31 :305–313. doi: 10.1016/j.chb.2013.10.048. [ CrossRef ] [ Google Scholar ]
• Lanza S.T., Cooper B.R. Latent profile analysis for developmental research. Child Development Perspectives. 2016; 10 (1):59–64. doi: 10.1111/cdep.12163. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Larose C., Harel O., Kordas K., Dey D.K. Latent class analysis of incomplete data via an entropy-based criterion. Statistical Methodology. 2016; 32 :107–121. doi: 10.1016/j.stamet.2016.04.004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Leung L. Predicting Internet risks: A longitudinal panel study of gratifications- sought, Internet addiction symptoms, and social media use among children and adolescents. Health Psychology and Behavioral Medicine: An Open Access Journal. 2014; 2 (1):424–439. doi: 10.1080/21642850.2014.902316. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luo T., Qin L., Cheng L., Wang S., Zhu Z., Xu J., et al. Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. Journal of Behavioral Addictions. 2021 doi: 10.1556/2006.2021.00025. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lyvers M., Narayanan S.S., Thorberg F.A. Disordered social media use and risky drinking in young adults: Differential associations with addiction-linked traits. Australian journal of psychology. 2019; 71 (3):223–231. doi: 10.1111/ajpy.12236. [ CrossRef ] [ Google Scholar ]
• Mabić, M., Gašpar, D., & Bošnjak, L. L. (2020). Social Media and Employment–Students' vs. Employers' Perspective. In  Proceedings of the ENTRENOVA-ENTerprise REsearch InNOVAtion Conference , 6(1), 482-492.
• Marmet S., Studer J., Wicki M., Bertholet N., Khazaal Y., Gmel G. Unique versus shared associations between self-reported behavioral addictions and substance use disorders and mental health problems: A commonality analysis in a large sample of young Swiss men. Journal of Behavioral Addictions. 2019; 8 (4):664–677. doi: 10.1556/2006.8.2019.70. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Martinac M., Karlović D., Babić D. Neuroscience of Alcohol. Academic Press; 2019. Alcohol and Gambling Addiction; pp. 529–535. [ CrossRef ] [ Google Scholar ]
• Meshi D., Elizarova A., Bender A., Verdejo-Garcia A. Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task. Journal of Behavioral Addictions. 2019; 8 (1):169–173. doi: 10.1556/2006.7.2018.138. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miller W.R., Forcehimes A.A., Zweben A. Guilford Publications; 2019. Treating addiction: A guide for professionals. [ Google Scholar ]
• Moretta T., Buodo G., Demetrovics Z., Potenza M.N. Tracing 20 years of research on problematic use of the internet and social media: Theoretical models, assessment tools, and an agenda for future work. Comprehensive Psychiatry. 2022; 112 doi: 10.1016/j.comppsych.2021.152286. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mourão R.R., Kilgo D.K. Black Lives Matter Coverage: How Protest News Frames and Attitudinal Change Affect Social Media Engagement. Digital Journalism. 2021; 1–21 doi: 10.1080/21670811.2021.1931900. [ CrossRef ] [ Google Scholar ]
• Nguyen M.H. LAB University of Applied Sciences; case: 2021. The impact of social media on students’ lives. [ Google Scholar ]
• Niedermoser D.W., Petitjean S., Schweinfurth N., Wirz L., Ankli V., Schilling H., et al. Shopping addiction: A brief review. Practice Innovations. 2021 doi: 10.1037/pri0000152. [ CrossRef ] [ Google Scholar ]
• Obar, J. A., & Wildman, S. S. (2015). Social media definition and the governance challenge- an introduction to the special issue. Obar, JA and Wildman, S.(2015). Social media definition and the governance challenge: An introduction to the special issue. Telecommunications policy , 39(9), 745-750. DOI: 10.1016/j.telpol.2015.07.014.
• Panova T., Carbonell X. Is smartphone addiction really an addiction? Journal of behavioral addictions. 2018; 7 (2):252–259. doi: 10.1556/2006.7.2018.49. 2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park C., Jun J., Lee T. Consumer characteristics and the use of social networking sites: A comparison between Korea and the US. International Marketing Review. 2015; 32 (3/4):414–437. doi: 10.1108/IMR-09-2013-0213. [ CrossRef ] [ Google Scholar ]
• Pontes H.M., Griffiths M.D. Internet addiction disorder and internet gaming disorder are not the same. Journal of Addiction Research & Therapy. 2014; 5 (4) doi: 10.4172/2155-6105.1000e124. [ CrossRef ] [ Google Scholar ]
• Pontes H.M., Griffiths M.D. Measuring DSM-5 Internet Gaming Disorder: Development and validation of a short psychometric scale. Computers in Human Behavior. 2015; 45 :137–143. doi: 10.1016/j.chb.2014.12.006. [ CrossRef ] [ Google Scholar ]
• Pontes H.M., Griffiths M.D. The development and psychometric properties of the Internet Disorder Scale–Short Form (IDS9-SF). Addicta . The Turkish Journal on Addictions. 2016; 3 (2) doi: 10.1016/j.addbeh.2015.09.003. [ CrossRef ] [ Google Scholar ]
• Prinstein M.J., Nesi J., Telzer E.H. Commentary: An updated agenda for the study of digital media use and adolescent development–future directions following Odgers & Jensen (2020) Journal of Child Psychology and Psychiatry. 2020; 61 (3):349–352. doi: 10.1111/jcpp.13190. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenberg J.M., Beymer P.N., Anderson D.J., Van Lissa C.J., Schmidt J.A. tidyLPA: An R package to easily carry out latent profile analysis (LPA) using open- source or commercial software. Journal of Open Source Software. 2019; 3 (30):978. doi: 10.21105/joss.00978. [ CrossRef ] [ Google Scholar ]
• Rose S., Dhandayudham A. Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors. Journal of Behavioral Addictions. 2014; 3 (2):83–89. doi: 10.1556/JBA.3.2014.003. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ryan J.M. Routledge; 2021. Timeline of COVID-19. COVID-19: Global pandemic, societal responses, ideological solutions , xiii-xxxii. [ Google Scholar ]
• Savci M., Aysan F. Technological addictions and social connectedness: Predictor effect of internet addiction, social media addiction, digital game addiction and smartphone addiction on social connectedness. Dusunen Adam: Journal of Psychiatry & Neurological Sciences. 2017; 30 (3):202–216. doi: 10.5350/DAJPN2017300304. [ CrossRef ] [ Google Scholar ]
• Saud M., Mashud M.I., Ida R. Usage of social media during the pandemic: Seeking support and awareness about COVID-19 through social media platforms. Journal of Public Affairs. 2020; 20 (4):e2417. [ Google Scholar ]
• Saunders J.B., Aasland O.G., Babor T.F., La Fuente De, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction. 1993; 88 (6):791–804. doi: 10.1111/j.1360-0443.1993.tb02093.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schivinski B., Brzozowska-Woś M., Buchanan E.M., Griffiths M.D., Pontes H.M. Psychometric assessment of the internet gaming disorder diagnostic criteria: An item response theory study. Addictive Behaviors Reports. 2018; 8 :176–184. doi: 10.1016/j.abrep.2018.06.004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Skinner H.A. The drug abuse screening test. Addictive Behaviors. 1982; 7 (4):363–371.https. doi: 10.1016/0306-4603(82)90005-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith T., Short A. Needs affordance as a key factor in likelihood of problematic social media use: Validation, Latent Profile Analysis and comparison of TikTok and Facebook problematic use measures. Addictive Behaviors. 2022; 107259 doi: 10.1016/j.addbeh.2022.107259. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spilkova J., Chomynova P., Csemy L. Predictors of excessive use of social media and excessive online gaming in Czech teenagers. Journal of Behavioral Addictions. 2017; 6 (4):611–619. doi: 10.1556/2006.6.2017.064. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Starcevic V. Behavioural addictions: A challenge for psychopathology and psychiatric nosology. Australian & New Zealand Journal of Psychiatry. 2016; 50 (8):721–725. doi: 10.1177/0004867416654009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sun Y., Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addictive Behaviors. 2020; 106699 doi: 10.1016/j.addbeh.2020.106699. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Szabo A., Pinto A., Griffiths M.D., Kovácsik R., Demetrovics Z. The psychometric evaluation of the Revised Exercise Addiction Inventory: Improved psychometric properties by changing item response rating. Journal of Behavioral Addictions. 2019; 8 (1):157–161. doi: 10.1556/2006.8.2019.06. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tein J.Y., Coxe S., Cham H. Statistical power to detect the correct number of classes in latent profile analysis. Structural equation modeling: a multidisciplinary journal. 2013; 20 (4):640–657. doi: 10.1080/10705511.2013.824781. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tong L.I., Saminathan R., Chang C.W. Uncertainty assessment of non-normal emission estimates using non-parametric bootstrap confidence intervals. Journal of Environmental Informatics. 2016; 28 (1):61–70. doi: 10.3808/jei.201500322. [ CrossRef ] [ Google Scholar ]
• Tullett-Prado D., Stavropoulos V., Mueller K., Sharples J., Footitt T.A. Internet Gaming Disorder profiles and their associations with social engagement behaviours. Journal of Psychiatric Research. 2021; 138 :393–403. doi: 10.1016/j.jpsychires.2021.04.037. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van den Eijnden R.J., Lemmens J.S., Valkenburg P.M. The social media disorder scale. Computers in Human Behavior. 2016; 61 :478–487. doi: 10.1177/0004867416654009. [ CrossRef ] [ Google Scholar ]
• Wang C.W., Ho R.T., Chan C.L., Tse S. Exploring personality characteristics of Chinese adolescents with internet-related addictive behaviors: Trait differences for gaming addiction and social networking addiction. Addictive Behaviors. 2015; 42 :32–35. doi: 10.1016/j.addbeh.2014.10.039. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wegmann E., Billieux J., Brand M. Mental health in a digital world . Academic Press; 2022. Internet-use disorders: A theoretical framework for their conceptualization and diagnosis; pp. 285–305. [ Google Scholar ]
• Winpenny E.M., Marteau T.M., Nolte E. Exposure of children and adolescents to alcohol marketing on social media websites. Alcohol and Alcoholism. 2014; 49 (2):154–159. doi: 10.1093/alcalc/agt174. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zarate D., Ball M., Montag C., Prokofieva M., Stavropoulos V. Unravelling the Web of Addictions: A Network Analysis Approach. Addictive Behaviors Reports. 2022; 100406 doi: 10.1016/j.abrep.2022.100406. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhong B., Huang Y., Liu Q. Mental health toll from the coronavirus: Social media usage reveals Wuhan residents’ depression and secondary trauma in the COVID-19 outbreak. Computers in Human Behavior. 2020; 114 doi: 10.1016/j.chb.2020.106524. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zilberman N., Yadid G., Efrati Y., Neumark Y., Rassovsky Y. Personality profiles of substance and behavioral addictions. Addictive Behaviors. 2018; 82 :174–181. doi: 10.1016/j.addbeh.2018.03.007. [ PubMed ] [ CrossRef ] [ Google Scholar ]

What was Trump found guilty of? See the 34 business records the jury decided he falsified

Donald Trump was found guilty of 34 felony counts of falsifying business records after prosecutors successfully convinced a jury he disguised hush money reimbursement as legal expenses. He is the first former president to be convicted of a crime.

Each count is tied to a different business record that prosecutors demonstrated Trump is responsible for changing to conceal or commit another crime .

Those records include 11 checks paid to former lawyer Michael Cohen , 11 invoices from Michael Cohen and 12 entries in Trump's ledgers.

The jury found that Trump authorized a plan to reimburse Cohen for the $130,000 hush money payment issued to Stormy Daniels and spread the payments across 12 months disguised as legal expenses.

Live updates: Former President Donald Trump found guilty on all counts in hush money case

Breakdown of 34 counts of falsifying business records

Here are the 34 business records Trump was found guilty of falsifying, as described in Judge Juan Merchan 's jury instructions :

• Count 1: Michael Cohen's invoice dated Feb. 14, 2017
• Count 2: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated Feb. 14, 2017
• Count 3: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated Feb. 14, 2017
• Count 4: A Donald J. Trump Revocable Trust Account check and check stub dated Feb. 14, 2017
• Count 5: Michael Cohen's invoice dated March 16, 2017
• Count 6: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated March 17, 2017
• Count 7: A Donald J. Trump Revocable Trust Account check and check stub dated March 17, 2017
• Count 8: Michael Cohen's invoice dated April 13, 2017
• Count 9: Entry in the Detail General Ledger for Donald J. Trump dated June 19, 2017
• Count 10: A Donald J. Trump account check and check stub dated June 19, 2017
• Count 11: Michael Cohen's invoice dated May 22, 2017
• Count 12: Entry in the Detail General Ledger for Donald J. Trump dated May 22, 2017
• Count 13: A Donald J. Trump account check and check stub May 23, 2017
• Count 14: Michael Cohen's invoice dated June 16, 2017
• Count 15: Entry in the Detail General Ledger for Donald J. Trump dated June 19, 2017
• Count 16: A Donald J. Trump account check and check stub dated June 19, 2017
• Count 17: Michael Cohen's invoice dated July 11, 2017
• Count 18: Entry in the Detail General Ledger for Donald J. Trump dated July 11, 2017
• Count 19: A Donald J. Trump account check and check stub dated July 11, 2017
• Count 20: Michael Cohen's invoice dated Aug. 1, 2017
• Count 21: Entry in the Detail General Ledger for Donald J. Trump dated Aug. 1, 2017
• Count 22: A Donald J. Trump account check and check stub dated Aug. 1, 2017
• Count 23: Michael Cohen's invoice dated Sept. 11, 2017
• Count 24: Entry in the Detail General Ledger for Donald J. Trump dated Sept. 11, 2017
• Count 25: A Donald J. Trump account check and check stub dated Sept. 12, 2017
• Count 26: Michael Cohen's invoice dated Oct. 18, 2017
• Count 27: Entry in the Detail General Ledger for Donald J. Trump dated Oct. 18, 2017
• Count 28: A Donald J. Trump account check and check stub dated Oct. 18, 2017
• Count 29: Michael Cohen's invoice dated Nov. 20, 2017
• Count 30: Entry in the Detail General Ledger for Donald J. Trump dated Nov. 20, 2017
• Count 31: A Donald J. Trump account check and check stub dated Nov. 21, 2017
• Count 32: Michael Cohen's invoice dated Dec. 1, 2017
• Count 33: Entry in the Detail General Ledger for Donald J. Trump dated Dec. 1, 2017
• Count 34: A check and check stub dated Dec. 5 2017

Jurors saw copies of these records entered as evidence. Evidence from the entire trial is available on the New York Courts website .

Contributing: Aysha Bagchi