Psychopathological profiles and trends of Italian social network users (Facebook, Instagram, Twitter, and TikTok)

Background and aim

With the advent of the Internet, digitization has increasingly taken the place of new mass communication. Thus, over the past two decades, rapid messaging services (chat) and social networks have become the most widely used means of meeting people and making friends, depending on their ability to geo-displace human relationships. New generations, in particular, born with these new forms of communication, have increasingly conformed to typical trends and styles; However, more and more people of all age groups have entered the world of social networks, making their active participation uniform. Despite such fluidity of communication and the possibility of making new friendly acquaintances, such contacts would still be conditioned by one’s subjective perceptions arising from individual personality profiles, just as would happen in any other communication [1].

The most widely used social networks studied here for the present research are [2].

1) Facebook (FB) [2]: Is a U.S. social media and social network, created on February 4, 2004, initially as a free university service and later expanded for commercial purposes, based on a Web 2.0 platform written in various programming languages (initially PHP, then Hack). It is available in over 100 languages (in Italian since May 14, 2008). The site, founded at Harvard University in the United States by Mark Zuckerberg, Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes, was originally designed exclusively for students at that university but was soon opened to students from other schools in the Boston area, the Ivy League, and Stanford University. It was later opened to high school students as well, and then to anyone claiming to be over 13 years of age, achieving tremendous worldwide success and profoundly changing many aspects related to socialization and interaction between individuals, both on a private level and a business and commercial level. It is possible to post content such as messages and videos, organize conventions and conferences, and watch content.

2) Instagram (IG): Is a U.S.-based social networking service that allows users to take photos, apply filters to them and share them via the Internet. The web application, developed by Kevin Systrom and Mike Krieger, was launched on October 6, 2010; initially available only on iOS, it later became compatible with iPhone, iPad, or iPod touch having iOS 3.1.2 or higher. As of April 3, 2012, it was also made available for devices supporting Android, from version 2.2 or higher. Instagram was originally distinguished for allowing only 1:1 images for iPhone display width. These restrictions were relaxed in 2015, with an increase to 1080 pixels. The service also added messaging features, the ability to include multiple images or videos in a single post, as well as “Stories”, similar to its main competitor Snapchat, which allows users to post photos and videos on a sequential feed, with each post accessible by others for 24 hours.

3) Twitter (TW): Is a news and microblogging service provided by the San Francisco, Inc.-based company Twitter, Inc. with branches in San Antonio and Boston. Twitter, Inc. was created in California but came under the jurisdiction of the state of Delaware in 2007. The network allows users to post short text messages of up to 280 characters (originally 140), called tweets, which are displayed on the user’s main page. Users can subscribe to other users’ tweets, this is called “following”, and subscribers are called followers, followers, and sometimes tweeps (Twitter + peeps, novice followers who have not yet made many tweets).

4) TikTok (TT): Is a Chinese social network launched in September 2016, initially under the name musical.ly. Through the app, users can create short music clips of varying lengths (from 15 to 600 seconds) and possibly change the playback speed, adding filters, special effects, and sounds to their videos. The app is different in China than the one released in the West and is more developed, even integrating functions for Internet marketing. Users can also add songs, sounds, or voices for dubbing. Unlike its international version, however, the Chinese version Douyin has built its e-commerce ecosystem. Suffice it to say that in China the app has its network of virtual stores and offers users its own online payment system called Douyin Pay.

Social network use has been correlated with both a significant increase in wellness and exercise activities [3,4] and marked sedentariness and a moderate increase in anxiety and depressive symptoms [5-10], especially concerning the perception of one’s body image [11,12], to eating [13] and sleep-wake disorders [14], fueling behavioral addictions [15] such as those to gaming [16], gambling [17], dysfunctional sexual behavior [18] and of social network use itself [19], but also the risk of self-harm [20] and suicide [21]. In particular, then, it has emerged that the content transmitted by “influencers,” regardless of the gradation of the quality of the message, negatively impacts well-being and perceived subjective quality [22-24], as the standards proposed are often above average and do not stand up to popular reality, offering false myths, unattainable beliefs and ideologies from easy gains with minimal effort of commitment (as much economic as physical and aesthetic health), fueling self-satisfaction and activating a competitive and imitative spiral rarely accessible to third.

The present research hypothesizes the existence of a link between the choice of the use of a specific social network by the subject user of the telematics service and his or her eventual psychopathological profile; in particular, referring to the psychopathological classification underlying the PICI model [25-30] and based on observational experiential evaluations, it is hypothesized that: Facebook (FB) users have a higher level of neurotic and psychotic dysfunctional traits [31-35], while Instagram (IG), Twitter (TW) and TikTok (TT) users have a higher level of borderline dysfunctional traits [36-39].

Materials and methods

Starting from the classic definition of “psychopathological profile” and “social network”, a population sample was selected for the administration of the following clinical instruments: 1) Clinical interview, based on narrative-anamnestic and documentary evidence and the basis of the Perrotta Human Emotions Model (PHEM) [40] concerning their emotional and perceptual-reactive experience; 2) Administration of the battery of psychometric tests published in international scientific journals by the author of this work: a) Perrotta Integrative Clinical Interviews (PICI-2), to investigate functional and dysfunctional personality traits; b) Perrotta Individual Sexual Matrix Questionnaire (PSM-Q) [41], to investigate individual sexual matrix (only section d); c) Perrotta Affective Dependence Questionnaire (PAD-Q) [42], to investigate affective and relational dependence profiles; d) Perrotta Human Defense Mechanisms Questionnaire (PDM-Q) [43], to investigate ego defense mechanisms. The results were then reprocessed using SPSS, the Anova test (with Bonferroni).

The phases of the research were divided as follows: 1) Selection of the population sample, according to the parameters indicated in the following paragraph. 2) Clinical interview, with each population group. 3) Administration of psychometric tests. 4) Data processing following administration. 5) Comparison of data obtained.

Setting and participants

The requirements decided for the selection of the sample population are 1) Age between 18 years and 60 years, divided into 4 age groups (18-25, 26-37, 38-46, 47-60) and by four different social networks (Facebook, Instagram, Twitter, TikTok). 2) Absence of psychopathological symptoms or confirmed diagnoses. 3) Italian nationality, with Italian ancestors in the last two generations. 4) Statement by the participating subject regarding his or her status as a “user” of Facebook (FB), Instagram (IG), Twitter (TW), or TikTok (TT), with a profile that has been active for at least 6 months and regularly updated daily through posting (quoting text, personal photos and videos, and technological manipulation interventions through dedicated apps) or chat activity or interaction with other users of the same platform. If the same user has multiple profiles on more than one social, he or she is asked to choose the profile that he or she uses the most or that he or she feels reflects him or her the most or that otherwise represents his or her prototypical type of use for the subjective ways and purposes pursued.

The selected setting, taking into account the protracted pandemic period (already in progress since the beginning of the present research), is the online platform via Skype and Videocall Whatsapp, both for the clinical interview and for the administration, during the two clinical interviews per patient (one for history and one for test administration), then entering the data into an excel database. The present research work was carried out from March 2020 to September 2022. All participants were guaranteed anonymity and the ethical requirements of the Declaration of Helsinki are met. Since the research is not financed by anyone, it is free of conflicts of interest. The selected population clinical sample, which meets the requirements, is 5,581 participants, divided into four groups Tables 1,2.

Results and discussion

Introduction

For the sake of ease of exposition, the examination of the results and discussions will be approached by dividing the data for the respective four selected social networks, and within each section, the data will be further divided into sixteen different subgroups (by age group: FB-1, FB-2, FB-3, FB-4, IG-1, IG-2, IG-3, IG-4, TW-1, TW-2, TW-3, TW-4, TT-1, TT-2, TT-3, TT-4) for the following test administrations: 1) PICI-2 (test of dysfunctional personality traits). 2) PAD-Q (test of affective dependence). 3) PDM-Q (test of ego defense mechanism functioning). 4) PSM-Q (test of individual sexual matrix identification, section d). Discussion of the results obtained will then follow for each subsection.

Facebook (FB)

FB-1: The subgroup consists of 188/759 (24.8%) participants (82 m / 106 f) of the total sample referring to Facebook (FB) Social Network Table 3.

FB-2: The subgroup consists of 226/759 (29.8%) participants (104 m / 122 f) of the total sample referring to Facebook (FB) Social Network Table 4.

FB-3: The subgroup consists of 231/759 (30.4%) participants (133 m / 98 f) of the total sample referring to Facebook (FB) Social Network Table 5.

FB-4: The subgroup consists of 114/759 (15.0%) participants (61 m / 53 f) of the total sample referring to Facebook (FB) Social Network Table 6.

Instagram (IG)

IG-1: The subgroup consists of 480/1,684 (28.5%) participants (212 m / 268 f) of the total sample referring to Instagram (IG) Social Network Table 7.

IG-2: The subgroup consists of 520/1,684 (30.9%) participants (232 m / 288 f) of the total sample referring to Instagram (IG) Social Network Table 8.

IG-3: The subgroup consists of 390/1,684 (23.1%) participants (168 m / 222 f) of the total sample referring to Instagram (IG) Social Network Table 9.

IG-4: The subgroup consists of 294/1,684 (17.5%) participants (126 m / 168 f) of the total sample referring to Instagram (IG) Social Network Table 10.

Twitter (TW)

TW-1: The subgroup consists of 282/1,006 (28.1%) participants (134 m / 148 f) of the total sample referring to Twitter (TW) Social Network Table 11.

TW-2: The subgroup consists of 326/1,006 (32.4%) participants (154 m / 172 f) of the total sample referring to Twitter (TW) Social Network Table 12.

TW-3: The subgroup consists of 262/1,006 (26.0%) participants (132 m / 130 f) of the total sample referring to Twitter (TW) Social Network Table 13.

TW-4: The subgroup consists of 136/1,006 (13.5%) participants (64 m / 72 f) of the total sample referring to Twitter (TW) Social Network Table 14.

TikTok (TT)

TT-1: The subgroup consists of 670/2,132 (31.4%) participants (244 m / 426 f) of the total sample referring to TitTok (TT) Social Network Table 15.

TT-2: The subgroup consists of 626/2,132 (29.4%) participants (258 m / 368 f) of the total sample referring to TitTok (TT) Social Network Table 16.

TT-3: The subgroup consists of 528/2,132 (24.8%) participants (218 m / 310 f) of the total sample referring to TitTok (TT) Social Network Table 17.

TT-4: The subgroup consists of 308/2,132 (14.4%) participants (124 m / 184 f) of the total sample referring to TitTok (TT) Social Network Table 18.

Conclusion

The present research showed that, on average, the selected users in the studied population sample, divided into sixteen subgroups, present in 79.9% of cases a psychopathological personality profile with at least 5 dysfunctional traits among the first three social networks analyzed (Facebook and Twitter with a lower frequency than Instagram, while TikTok users present an average value of 95.5% of cases).

Equally distributed are also the hypotheses of affective addiction among users of the four social networks, with an average of 41.7 per cent of cases, although always with higher pathological peaks in the case of Instagram, and even more for Tiktok, which varies with a much higher average of 69.2%.

The pronounced dysfunctional tendency found is also confirmed by the tests related to the study of ego defence mechanisms, which in 100 per cent of the psychopathological cases detected with the PICI (Perrotta Integrative Clinical Interviews) model turn out to be markedly dysfunctional, especially concerning the mechanisms of isolation, fixation, identification, denial, repression, regression, omnipotence, idealization and devaluation.

The survey on dysfunctional sexual behaviours also found the marked presence of the clinical condition of users, with a mean value of 21.3% for Twitter, 55.9% for Facebook, 57.8% for Instagram, and 81% for TikTok; in particular, the presence of pedophilic paraphilia/pederasty is found in Instagram users with a mean value of 28.5% and for TikTok with a mean value of 43%.

The only comparisons that do not appear to be statistically significant are the PICI comparisons between FB-IG, FB-TW, and IG-TW and the PDM comparisons between FB-TW, FB-TT, and TW-TT; all other comparisons appear to be highly significant.

Therefore, it is concluded that there is a correlation between the choice of preferred profile on a specific social network and the psychopathological personality profile: Facebook users are more oriented toward the neurotic (anxious-phobic, somatic and obsessive) and borderline (borderline and depressive) area, Twitter users toward the borderline (bipolar, borderline and narcissistic) area, and Instagram and TikTok users toward the borderline (bipolar, borderline, histrionic, antisocial, psychopathic and narcissistic) and psychotic (delusional, paranoid and dissociative) area.

1. Perrotta G, Fabiano G. Behavioural disorders in children and adolescents: Definition, clinical contexts, neurobiological profiles and clinical treatments. Open J Pediatr Child Health. 2021; 6(1): 005-015. DOI: 10.17352/ ojpch.000030.
2. Perrotta G. Psicologia generale. Luxco Ed. 2019.
3. Duregon F, Bullo V, Di Blasio A, Cugusi L, Pizzichemi M, Sciusco S, Viscioni G, Cruz-Diaz D, Bocalini DS, Bortoletto A, Favro F, Alberton CL, Gobbo S, Bergamin M. The Role of Facebook® in Promoting a Physically Active Lifestyle: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2022 Aug 9;19(16):9794. doi: 10.3390/ijerph19169794. PMID: 36011447; PMCID: PMC9408066.
4. Gu S, Ping J, Xu M, Zhou Y. TikTok browsing for anxiety relief in the preoperative period: A randomized clinical trial. Complement Ther Med. 2021 Aug;60:102749. doi: 10.1016/j.ctim.2021.102749. Epub 2021 Jun 9. PMID: 34118388.
5. Huang C. A meta-analysis of the problematic social media use and mental health. Int J Soc Psychiatry. 2022 Feb;68(1):12-33. doi: 10.1177/0020764020978434. Epub 2020 Dec 9. PMID: 33295241.
6. Perrotta G. Anxiety disorders: definitions, contexts, neural correlates and strategic therapy. J Neurol Neurosci. 2019; 6(1):046.
7. Perrotta G. Depressive disorders: Definitions, contexts, differential diagnosis, neural correlates and clinical strategies. Arch Depress Anxiety. 2019; 5(2):009-033. DOI: 10.17352/2455-5460.000038.
8. Lambert J, Barnstable G, Minter E, Cooper J, McEwan D. Taking a One-Week Break from Social Media Improves Well-Being, Depression, and Anxiety: A Randomized Controlled Trial. Cyberpsychol Behav Soc Netw. 2022 May;25(5):287-293. doi: 10.1089/cyber.2021.0324. Epub 2022 May 3. PMID: 35512731.
9. Ivie EJ, Pettitt A, Moses LJ, Allen NB. A meta-analysis of the association between adolescent social media use and depressive symptoms. J Affect Disord. 2020 Oct 1;275:165-174. doi: 10.1016/j.jad.2020.06.014. Epub 2020 Jun 24. PMID: 32734903.
10. Perrotta G. Maladaptive stress: Theoretical, neurobiological and clinical profiles. Arch Depress Anxiety. 2021; 7(1): 001-007. DOI: 10.17352/2455-5460. 000057.
11. Tiggemann M, Hayden S, Brown Z, Veldhuis J. The effect of Instagram "likes" on women's social comparison and body dissatisfaction. Body Image. 2018 Sep;26:90-97. doi: 10.1016/j.bodyim.2018.07.002. Epub 2018 Jul 21. PMID: 30036748.
12. Perrotta G. The concept of altered perception in "body dysmorphic disorder": the subtle border between the abuse of selfies in social networks and cosmetic surgery, between socially accepted dysfunctionality and the pathological condition. J Neurol Neurol Sci Disord. 2020; 6(1): 001-007. DOI: 10.17352/jnnsd.000036.
13. Perrotta G. Neural correlates in eating disorders: Definition, contexts and clinical strategies. J Pub Health Catalog. 2019; 2(2): 137-148.
14. Perrotta G. Sleep-wake disorders: Definition, contexts and neural correlations. J Neurol Psychol. 2019; 7(1):09.
15. Perrotta G. Behavioral addiction disorder: definition, classifications, clinical contexts, neural correlates and clinical strategies. J Addi Adol Beh. 2019; 2(1). DOI: 10.31579/ JARAB.19/007.
16. Perrotta G. Internet gaming disorder in young people and adolescent: a narrative review. J Addi Adol Beh. 2019; 2(2). DOI: 10.31579-007/2688-7517/013.
17. Perrotta G. Pathological gambling in adolescents and adults: definition, clinical contexts, differential diagnosis, neural correlates and therapeutic approaches. ES J Neurol. 2020; 1(1): 1004.
18. Perrotta G. Dysfunctional sexual behaviours: definition, clinical contexts, neurobiological profiles and treatments. Int J Sex Reprod Health Care. 2020; 3(1): 061-069. DOI: 10.17352/ijsrhc.000015.
19. Cheng C, Lau YC, Chan L, Luk JW. Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values. Addict Behav. 2021 Jun;117:106845. doi: 10.1016/j.addbeh.2021.106845. Epub 2021 Jan 26. PMID: 33550200.
20. Miguel EM, Chou T, Golik A, Cornacchio D, Sanchez AL, DeSerisy M, Comer JS. Examining the scope and patterns of deliberate self-injurious cutting content in popular social media. Depress Anxiety. 2017 Sep;34(9):786-793. doi: 10.1002/da.22668. Epub 2017 Jun 29. PMID: 28661053.
21. Perrotta G. Suicidal risk: definition, contexts, differential diagnosis, neural correlates and clinical strategies. J. Neuroscience and Neurological Surgery. 2020; 6(2): 114. DOI: 10.31579/2688-7517/114.
22. Lowe-Calverley E, Grieve R. Do the metrics matter? An experimental investigation of Instagram influencer effects on mood and body dissatisfaction. Body Image. 2021 Mar;36:1-4. doi: 10.1016/j.bodyim.2020.10.003. Epub 2020 Oct 21. PMID: 33099189.
23. Wood M, Pila E. Investigating the effects of fit-normative and weight-inclusive Instagram images on women's exercise motivations. Body Image. 2022 Jun;41:460-471. doi: 10.1016/j.bodyim.2022.04.003. Epub 2022 May 17. PMID: 35594800.
24. Sampson A, Jeremiah HG, Andiappan M, Newton JT. The effect of viewing idealised smile images versus nature images via social media on immediate facial satisfaction in young adults: A randomised controlled trial. J Orthod. 2020 Mar;47(1):55-64. doi: 10.1177/1465312519899664. Epub 2020 Feb 7. PMID: 32031041.
25. Perrotta G. The structural and functional concepts of personality: The new Integrative Psychodynamic Model (IPM), the new Psychodiagnostic Investigation Model (PIM) and the two clinical interviews for the analysis of personality disorders (Perrotta Integrative Clinical Interview or PICI) for adults and teenagers (1TA version) and children (1C version). Psychiatry Peertechz; e-book. 2020. DOI: 10.17352/ebook10118.
26. Perrotta G. First revision of the Psychodiagnostic Investigation Model (PIM-1R) and elaboration proposal of a clinical interview for the analysis of personality disorders (Perrotta Integrative Clinical Interview or PICI-1) for adults, teenagers and children. Psychiatry Peertechz; e-book. 2020. DOI: 10.17352/ebook10119.
27. Perrotta G. "Perrotta Integrative Clinical Interview (PICI-1)": Psychodiagnostic evidence and clinical profiles in relation to the MMPI-II. Ann Psychiatry Treatm. 2020; 4(1): 062-069. DOI: 10.17352/apt.000022.
28. Perrotta G. "Perrotta Integrative Clinical Interview" (PICI) for adults and teenagers (1TA version) and children (1C version): new theoretical models and practical integrations between the clinical and psychodynamic approach. Ann Psychiatry Treatm. 2021; 5(1): 001-014. DOI: 10.17352/ apt.000024.
29. Perrotta G. Perrotta Integrative Clinical Interview (PICI-1): a new revision proposal for PICI-1TA. Two single cases. Glob J Medical Clin Case Rep. 2021; 8(1):041-049. DOI: 10.17352/2455-5282-000125.
30. Perrotta G. Perrotta Integrative Clinical Interviews (PICI-2): innovations to the first model, the study on the new modality of personological investigation, trait diagnosis and state diagnosis, and the analysis of functional and dysfunctional personality traits. An integrated study of the dynamic, behavioural, cognitive and constructivist models in psychopathological diagnosis. Ann Psychiatry Treatm. 2021; 5(1): 067-083. DOI: 10.17352/apt.000033.
31. Perrotta G. Obsessive-Compulsive Disorder: definition, contexts, neural correlates and clinical strategies. Journal of Neurology. 2019; 1.4: 08-16.
32. Perrotta G. Avoidant personality disorder: Definition, clinical and neurobiological profiles, differential diagnosis and therapeutic framework. J Neuro Neurol Sci Disord. 2021; 7(1): 001-005. DOI: 10.17352/ jnnsd.000041.
33. Perrotta G. Delusions, paranoia and hallucinations: definitions, differences, clinical contexts and therapeutic approaches. Journal of Neurology (CJNE). 2019; 1.4: 22-28.
34. Perrotta G. The state of consciousness: from perceptual alterations to dissociative forms. Defining, neurobiological and clinical profiles. J Neuro Neurol Sci Disord. 2021; 7(1): 006-018. DOI: 10.17352/jnnsd.000042.
35. Perrotta G. Psychotic spectrum disorders: definitions, classifications, neural correlates and clinical profiles. Ann Psychiatry Treatm. 2020; 4(1): 070-084. DOI: 10.17352/apt.000023.
36. Perrotta G. Borderline Personality Disorder: definition, differential diagnosis, clinical contexts and therapeutic approaches. Ann Psychiatry Treatm. 2020; 4(1): 043-056. DOI: 10.17352/apt.000020.
37. Perrotta G. Bipolar disorder: definition, differential diagnosis, clinical contexts and therapeutic approaches. J Neuroscience Neurological Surgery. 2019; 5(1). DOI: 10.31579/2578-8868/097.
38. Perrotta G. Narcissism and psychopathological profiles: definitions, clinical contexts, neurobiological aspects and clinical treatments. J Clin Cases Rep. 2020; 4(85): 12-25. DOI: 10.46619/joccr.2021.S5-1003.
39. Perrotta G. Histrionic personality disorder: Definition, clinical profiles, differential diagnosis and therapeutic framework. Arch Community Med Public Health. 2021; 7(1): 001-005. DOI: 10.17352/2455-5479.000123.
40. Perrotta G. The "Human Emotions" and the "Perrotta Human Emotions Model" (PHEM): The new theoretical model. Historical, neurobiological and clinical profiles. Arch Depress Anxiety. 2021; 7(2): 020-027. DOI: 10.17352/2455-5460.000062.
41. Perrotta G. Perrotta Individual Sexual Matrix Questionnaire (PSM-1). The new clinical questionnaire to investigate the main areas of the individual sexual matrix. Int J Sex Reprod Health Care. 2021; 4(1): 013-021. DOI: 10.17352/ ijsrhc.000020.
42. Perrotta G. Perrotta Affective Dependence Questionnaire (PAD-Q): Clinical framing of the affective-sentimental relational maladaptive model. Ann Psychiatry Treatm. 2021; 5(1): 062-066. DOI: 10.17352/apt.000032.
43. Perrotta G. “Perrotta Human Defense Mechanisms Questionnaire” (PDM-Q): The new psychodiagnostic tool to identify human psychological defense mechanisms and their clinical implications. Arch Depress Anxiety. 2021; 7(2): 029-033. DOI: 10.17352/2455-5460.000063.