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Me, myself-itis: A brief overview of obsessive selfie-taking

According to the Oxford English Dictionary, a selfie is a “photograph that one has taken of oneself, typically one taken with a smartphone or webcam and shared via social media”. From a psychological perspective, the taking of selfies is a self-oriented action that allows users to establish their individuality and self-importance; it is also associated with personality traits such as narcissism.

However, selfie-taking is more than just the taking of a photograph. It can include the editing of the color and contrast, the changing of backgrounds, and the addition of other effects before uploading. These added options and the use of integrative editing have further popularized selfie-taking behavior, particularly amongst teenagers and young adults.

On March 31, 2014, a story appeared on a website called the Adobo Chronicles that claimed that the American Psychiatric Association (APA) had classed “selfitis” as a new mental disorder. According to the author, the organization had defined selfitis as “the obsessive compulsive desire to take photos of one’s self and post them on social media as a way to make up for the lack of self-esteem and to fill a gap in intimacy”. The same article also claimed there three levels of the disorder: borderline (“taking photos of one’s self at least three times a day but not posting them on social media”), acute (“taking photos of one’s self at least three times a day and posting each of the photos on social media”), and chronic (“uncontrollable urge to take photos of one’s self round the clock and posting the photos on social media more than six times a day”).

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The story was republished on numerous news sites around the world, but it soon became clear the story was a hoax. However, one of the reasons that so many news outlets republished the story – other than that it seemingly fit certain preexisting stereotypes in people’s minds – was that the criteria used to delineate the three levels of selfitis (i.e., borderline, acute, and chronic) seemed believable.

Therefore, we thought it would be interesting to examine whether there was any substance to the claims that taking selfies can be a time-consuming and potentially obsessive behavior – the stereotype underlying many people’s credulity about the fake story. We empirically explored the concept of selfitis across two studies and collected data on the existence of selfitis with respect to the three alleged levels (borderline, acute, and chronic), ultimately developed our own psychometric scale to assess the sub-components of selfitis (the Selfitis Behaviour Scale).

We used Indian students as participants in our research because India has the largest total number of users on Facebook by country. We also knew India accounts for more selfie-related deaths in the world compared to any other country. with a reported 76 deaths reported out of a total of 127 worldwide since 2014. (Those deaths usually occur when people attempt to take selfies in dangerous contexts, such as in water, from heights, in the proximity of moving vehicles, like trains, or while posing with weapons).

Our study began by using focus group interviews with 225 young adults with an average age of 21 years old to gather an initial set of criteria that underlie selfitis. Example questions used during the focus group interviews included ‘What compels you to take selfies?’, ‘Do you feel addicted to taking selfies?’ and ‘Do you think that someone can become addicted to taking selfies?’ It was during these interviews that participants confirmed there appeared to be individuals who obsessively take selfies — or, in other words, that selfitis does at least exist. But, since we did not collect any data on the negative psychosocial impacts, we cannot yet claim that the behavior is a mental disorder; negative consequences of the behavior is a key part of that determination.

The six components of selfitis, tested on the further participants, were: environmental enhancement (e.g., taking selfies in specific locations to feel good and show off to others); social competition (e.g., taking selfies to get more ‘likes’ on social media); attention-seeking (e.g., taking selfies to gain attention from others); mood modification (e.g., taking selfies to feel better); self-confidence (e.g., taking selfies to feel more positive about oneself); and subjective conformity (e.g., taking selfies to fit in with one’s social group and peers).

Our findings showed that those with chronic selfitis were more likely to be motivated to take selfies due to attention-seeking, environmental enhancement and social competition. The results suggest that people with chronic levels of selfitis are seeking to fit in with those around them, and may display symptoms similar to other potentially addictive behaviours. Other studies have also suggested that a minority of individuals might have a ‘selfie addiction’ (see ‘References and further reading’ below).

With the existence of the condition apparently confirmed, we hope that further research will be carried out to understand more about how and why people develop this potentially obsessive behaviour, and what can be done to help people who are the most affected. However, the findings of our research do not indicate that selfitis is a mental disorder based on the findings of this study – a claim made in many of the news reports about our study, possibly demonstrating how deep the stereotypes about selfie-takes run – only that selfitis appears to be a condition that requires further research to fully assess the psychosocial impacts that the behaviour might have on the individual.

If you are interested in assessing your own behavior, click here to download where you can complete the self-assessment test in the Appendix of our paper.

Please note: This article was co-written with Dr. Janarthanan Balakrishnan (Thiagarajar School of Management, India)

Dr Mark Griffiths, Professor of Behavioural Addiction, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Balakrishnan, J. & Griffiths, M.D. (2018). An exploratory study of ‘selfitis’ and the development of the Selfitis Behavior Scale. International Journal of Mental Health and Addiction, https://doi.org/10.1007/s11469-017-9844-x.

Gaddala, A., Hari Kumar, K. J., & Pusphalatha, C. (2017). A study on various effects of internet and selfie dependence among undergraduate medical students. Journal of Contemporary Medicine and Dentistry, 5(2), 29-32.

Griffiths, M.D. & Balakrishnan, J. (2018). The psychosocial impact of excessive selfie-taking in youth: A brief overview. Education and Health, 36(1), 3-5.

Kaur, S., & Vig, D. (2016). Selfie and mental health issues: An overview. Indian Journal of Health and Wellbeing, 7(12), 1149

Khan, N., Saraswat, R., & Amin, B. (2017). Selfie: Enjoyment or addiction? Journal of Medical Science and Clinical Research, 5, 15836-15840.

Lee, R. L. (2016). Diagnosing the selfie: Pathology or parody? Networking the spectacle in late capitalism. Third Text, 30(3-4), 264-27

Senft, T. M., & Baym, N. K. (2015). Selfies introduction – What does the selfie say? Investigating a global phenomenon. International Journal of Communication, 9, 19

Singh, D., & Lippmann, S. (2017). Selfie addiction. Internet and Psychiatry, April 2. Located at: https://www.internetandpsychiatry.com/wp/editorials/selfie-addiction/

Singh, S. & Tripathi, K.M. (2017). Selfie: A new obsession. SSRN, Located at: http://dx.doi.org/10.2139/ssrn.2920945

The junkie generation? Teenage “addiction” to social media

Earlier today I appeared live on my local radio station (BBC Radio Nottingham) commenting on a study released by the Allen Carr Addiction Clinics (ACAC) concerning teenage addiction (and more specifically addiction to social media). The study was a survey of 1,000 British teenagers aged 12 to 18 years old and the press release went with the heading “INFO UK BREEDING A GENERATION OF TEENAGE ADDICTS SAYS NEW STUDY” (their capital letters, not mine) with the sub-headline that “83% of UK teenagers would struggle to go ‘cold turkey’ from social media and their other vices for a month”.

As someone that has spent almost 30 years studying ‘technological addictions’ I was interested in the survey’s findings. I tried to get hold of the actual report by contacting the ACAC Press Office. They were very helpful and sent me a copy of the Excel file containing the raw data (entitled ‘Addicted Britain’). They also informed me that the data were collected for ACAC by the market research company OnePoll, and that the teenagers filled out the survey online (with parents’ permission). However, there is no actual published report with the findings (and more importantly, no methodological details). I asked ACAC if they knew the response rate (for instance, was the online survey sent to 10,000 teenagers to get their 1,000 responses that would give a response rate of 10%), and how were the teenagers recruited in the first place. Also, as the survey was carried out online, those teenagers who are the most tech-savvy and feel confident online, would be more likely to participate than those who don’t like (or rarely use) online applications. Before I comment on the survey itself, I would just like to provide some excerpts from the press release that was sent out:

“The explosion of social media, selfies and mobile devices is priming a generation of UK teenagers for a lifelong struggle with addiction…83% of UK teenagers admit they would struggle to give up their vices for a whole month. [The study] unveiled a worrying trend of growing numbers of young people constantly striving to find the next thrill, mostly via technology and social media. When asked which behaviours they could abstain from, UK teens said they would most struggle living without texting (66%), followed by social networking (58%), junk food (28%) and alcohol (6%). The report found that the average teen checks social media 11 times a days, sends 17 text messages and takes a ‘selfie’ picture every four days. This constant pursuit of stimulation, peer approval, instant gratification, and elements of narcissism are all potential indicators of addictive behaviour. The study highlights that parents across the UK are inadvertently becoming ‘co-dependents’ enabling their child’s addictions by providing them with cash albeit with the best of intentions”.

The first thing that struck me reading this text was the use of the word “vice”. Most dictionary definitions of a vice is “immoral or wicked behaviour” or criminal activities involving prostitution, pornography, or drugs”. As far as I am concerned, social networking, junk food, and alcohol are not vices (especially social networking). The whole wording of the press release is written in a way to pathologise normal behaviours such as engaging in social media use. Also, asking teenagers about which behaviours they could not abstain from for a month tells us almost nothing about addiction. All it tells us is that the activities that teenagers most engage in are the ones they would find hardest not to do. This is just common sense. My main hobbies are listening to music on my i-Pod and reading. I would really have difficulty in not listening to my favourite music or reading for a whole month but I’m not addicted to music or reading.

The ACAC kindly sent me all the questions that were asked in the survey and there was no kind of addiction scale embedded in any of the questions asked. Basically, the survey does not investigate teenagers’ potential addictions, as no screening instrument for any behaviour asked about was included in the survey. There were some attitude questions asking whether activities like social networking could be addictive, but as I have argued in previous blogs, almost any activity that is constantly rewarding can be potentially addictive.

That’s not so say we shouldn’t be concerned about teenagers’ excessive use of technology as my own research has shown that a small minority of teenagers do appear to have problems and/or be addicted to various online activities. However, as my research has shown, doing something excessively doesn’t mean that it is addictive. As I have noted in a number of my academic papers, the difference between a healthy enthusiasm and an addiction is that healthy enthusiasm add to life and addictions take away from it. The perceived overuse of technology by the vast majority of teenagers is quite clearly something that is life-enhancing and positive with no detrimental effects whatsover.

Given that the vast majority of teenagers use the social media to communicate and interact with friends, I was surprised that ACAC’s findings were not closer to 100% saying that they couldn’t abstain for one month. Which teenagers would find it easy not to use social media for a month given how important it is in their day-to-day social lives? The findings in the press release also quote John Dicey (Global Managing Director and Senior Therapist of ACAC) who said:

“The findings of this report are cause for concern and highlight a generation of young people exhibiting many of the hallmarks of addictive behaviour. The explosion of technology we have seen since the late 90’s offers incredible opportunities to our youth – the constant stimulation provided by access to the internet for example can be a good or a bad thing. There’s a price to pay. This study indicates that huge numbers of young people are developing compulsions and behaviours that they’re not entirely in control of and cannot financially support. Unless we educate our young people as to the dangers of constant stimulation and consumption, we are sleepwalking towards an epidemic of adulthood addiction in the future”.

While my own research shows that a small minority of teenagers experience problems concerning various online activities, there was almost nothing in the ACAC report “huge numbers of young people are developing compulsions and behaviours that they’re not entirely in control of”. The use of the word “huge” is what we psychologists call a ‘fuzzy quantifier’ (as what is ‘huge’ to one person may not be ‘huge’ to another). Mr. Dicey’s conclusions simply cannot be made from the data collected. He says that the report shows that many teenagers are displaying the “hallmarks of addictive behaviour” but given no addiction screening instruments were used, the data do not show this. The press release uses the following findings to make the claim that “the abundance of technology that UK teens can access seems to be creating a generation of ‘tech addicts’!”

“One-third of UK teens (32%) admit they check social media more than 10 times a day. The report also found that the average teen checks social media 11 times day, which equals once every 1.5 hours they are awake. UK teens are also avid takers of ‘selfies’, with over a quarter taking more than 10 a month. The average teen takes 7.4 selfies a month, equalling one every four days on average…The plethora of technology available to teens is also having a worrying impact on their attention spans. 1 in 4 teens have over 20 apps on their smartphones, with the average teen having 13 apps on their device. The constant search for the ‘next thing’ is evidenced in how they use apps – 46% admitted that they stop using or delete an app less than a week after using it, freeing up storage space for a new app”.

Anyone that has teenagers (I have three screenagers myself) will tell you that the above statistics indicate adolescent normality not addiction. Checking social media 10 times a day does not indicate addiction in the slightest. Although I have never taken a selfie, I check my social media far more than 10 times a day. Deleting apps to make way for other apps is no different from me removing songs on my i-Pod every week to make way for other songs I want to listen to. Again, there is absolutely nothing in these statistics that provides evidence of adolescent addiction.

Anyone that is aware of my work will know that I take the issue of teenage technology use seriously and that I firmly believe that a small minority of adolescents experience addiction to various online applications. However, studies like the one done for ACAC do little for the area as the rhetoric of the claims are unsupported by their data.

Dr. Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Griffiths, M.D. (2010). The role of context in online gaming excess and addiction: Some case study evidence. International Journal of Mental Health and Addiction, 8, 119-125.

Griffiths, M.D., King, D.L. & Delfabbro, P.H. (2014). The technological convergence of gambling and gaming practices. In Richard, D.C.S., Blaszczynski, A. & Nower, L. (Eds.). The Wiley-Blackwell Handbook of Disordered Gambling (pp. 327-346). Chichester: Wiley.

Griffiths, M.D., King, D.L. & Demetrovics, Z. (2014). DSM-5 Internet Gaming Disorder needs a unified approach to assessment. Neuropsychiatry, 4(1), 1-4.

Griffiths, M.D., Kuss, D.J. & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In K. Rosenberg & L. Feder (Eds.), Behavioral Addictions: Criteria, Evidence and Treatment (pp.119-141). New York: Elsevier.

Griffiths, M.D., Kuss, D.J. & King, D.L. (2012). Video game addiction: Past, present and future. Current Psychiatry Reviews, 8, 308-318.

Griffiths, M.D. & Pontes, H.M. (2014). Internet addiction disorder and internet gaming disorder are not the same. Journal of Addiction Research and Therapy, 5: e124. doi:10.4172/2155-6105.1000e124.

Kuss, D.J. & Griffiths, M.D. (2011). Addiction to social networks on the internet: A literature review of empirical research. International Journal of Environmental and Public Health, 8, 3528-3552.

Kuss, D.J., Griffiths, M.D. & Binder, J. (2013). Internet addiction in students: Prevalence and risk factors. Computers in Human Behavior, 29, 959-966.

Kuss, D.J., Griffiths, M.D., Karila, L. & Billieux, J. (2014).  Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20, 4026-4052.

Kuss, D.J., van Rooij, A.J., Shorter, G.W., Griffiths, M.D. & van de Mheen, D. (2013). Internet addiction in adolescents: Prevalence and risk factors. Computers in Human Behavior, 29, 1987-1996.