Adult content consumption in online social networks

Abstract

Users in online social networks naturally organize themselves into overlapping and interlinked communities that are formed around common identity or shared topical interests. Some communities gather people around specific deviant behaviors, conducts that are commonly considered inappropriate with respect to the society’s norms or moral standards such as drug use, eating disorders, and pornographic content consumption. From a network analysis perspective, the set of interactions between members of these communities form deviant networks that map how the deviant content is shared and consumed. It is commonly believed that deviant networks are small and isolated from the mainstream social media life; accordingly, most research studies have considered them in isolation. We focus on adult content consumption networks, which is one deviant network with a significant presence in online social media and in the Web in general. We investigate two large online social networks and discuss the following insights. Deviant networks are limited in size, tightly connected and structured in subgroups. Nevertheless, content originated in deviant networks spreads widely across the whole social graph possibly touching a large number of unintentionally exposed users, such that the average local perception is that neighboring users share more deviant content. Finally, we investigate how content production and consumption vary with age and show that the consumption rate is very similar between male and female users up to the age of 25. We conclude that deviant communities are deeply rooted into the relational fabric of a social network, and that a deeper understanding of how their activity impacts on every other user is required.

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Notes

  1. 1.

    Flickr users can mark their own photographs as “adult”. We first attempted to use this self-reported information to detect adult photographs. We found that this approach leads to many false positives, mainly because very often pictures are marked in big batches containing adult and non-adult pictures.

  2. 2.

    Louvain is a modularity-based graph clustering algorithm that shows very good performance across several benchmarks (Fortunato 2010) and that is fast to compute even on large networks.

  3. 3.

    Link directionality is considered: ties originate from groups listed on the rows and land on groups listed on the columns.

  4. 4.

    The number of samples in each age distribution is high; therefore, as expected, all the differences between the average values are statistically significant (\(p<0.01\)) under the Mann–Whitney test.

References

  1. Adamic LA, Glance N (2005) The political blogosphere and the 2004 us election: divided they blog. In: International workshop on Link discovery. ACM

  2. Aiello LM (2015) Group types in social media. In: Paliouras G, Papadopoulos S, Vogiatzis D, Kompatsiaris Y (eds) User community discovery. Human-computer interaction series. Springer, Berlin, pp 97–134

    Google Scholar 

  3. Aiello LM, Barrat A, Cattuto C, Ruffo G, Schifanella R (2010) Link creation and profile alignment in the aNobii social network. In: SocialCom

  4. Aiello LM, Barrat A, Schifanella R, Cattuto C, Markines B, Menczer F (2012) Friendship prediction and homophily in social media. ACM Trans Web 6(2):1–33

    Article  Google Scholar 

  5. Allen M, D’Alessio D, Brezgel K (1995) A meta-analysis summarizing the effects of pornography II aggression after exposure. Hum Commun Res 22(2):258–283

    Article  Google Scholar 

  6. Ashforth BE, Mael F (1989) Social identity theory and the organization. Acad Manag Rev 14(1):20–39

    Google Scholar 

  7. Attwood F (2005) What do people do with porn? Qualitative research into the consumption, use, and experience of pornography and other sexually explicit media. Sex Cult 9(2):65–86

    Article  Google Scholar 

  8. Barbieri N, Bonchi F, Manco G (2013) Cascade-based community detection. In: WSDM. ACM

  9. Bessi A, Coletto M, Davidescu GA, Scala A, Caldarelli G, Quattrociocchi W (2015) Science vs conspiracy: collective narratives in the age of misinformation. PloS one 10(2):02

    Article  Google Scholar 

  10. Blackburn J, Simha R, Kourtellis N, Zuo X, Ripeanu M, Skvoretz J, Iamnitchi A (2012) Branded with a scarlet c: cheaters in a gaming social network. In: WWW: proceedings of the 21st international conference on World Wide Web. ACM, New York, NY, USA, pp 81–90

  11. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008

    Article  Google Scholar 

  12. Boero N, Pascoe CJ (2012) Pro-anorexia communities and online interaction: bringing the pro-ana body online. Body Soc 18(2):27–57

    Article  Google Scholar 

  13. Buzzell T (2005) Demographic characteristics of persons using pornography in three technological contexts. Sex Cult 9(1):28–48

    Article  Google Scholar 

  14. Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. Icwsm 10(10–17):30

    Google Scholar 

  15. Chen A-S, Leung M, Chen C-H, Yang SC (2013) Exposure to internet pornography among taiwanese adolescents. Soc Behav Pers Int J 41(1):157–164

    Article  Google Scholar 

  16. Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on World Wide Web. ACM, pp 925–936

  17. Christakis NA, Fowler JH (2008) The collective dynamics of smoking in a large social network. N Engl J Med 358(21):2249–2258

    Article  Google Scholar 

  18. Clinard M, Meier R (2015) Sociology of deviant behavior. Wadsworth Cengage Learning, Boston

    Google Scholar 

  19. Coletto M, Aiello LM, Lucchese C, Silvestri F (2016) On the behaviour of deviant communities in online social networks. In: ICWSM 2016. Köln, Germany

  20. Conover M, Ratkiewicz J, Francisco M, Gonçalves B, Menczer F, Flammini A (2011) Political polarization on twitter. In: ICWSM

  21. Davis JP (2002) The experience of ‘bad’behavior in online social spaces: a survey of online users. Social Computing Group, Microsoft Research, Redmond

    Google Scholar 

  22. De Choudhury M (2015) Anorexia on tumblr: a characterization study. In: Digital health. ACM

  23. Dolev S, Elovici Y, Puzis R (2010) Routing betweenness centrality. J ACM (JACM) 57(4):25

    MathSciNet  Article  MATH  Google Scholar 

  24. Dow PA, Adamic LA, Friggeri A (2013) The anatomy of large facebook cascades. In: ICWSM

  25. Dunlop PD, Lee K (2004) Workplace deviance, organizational citizenship behavior, and business unit performance: the bad apples do spoil the whole barrel. J Organ Behav 25(1):67–80

    Article  Google Scholar 

  26. Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge

    Google Scholar 

  27. Feld SL (1991) Why your friends have more friends than you do. Am J Sociol 96(6):1464–1477

    Article  Google Scholar 

  28. Feller A, Kuhnert M, Sprenger TO, Welpe IM (2011) Divided they tweet: the network structure of political microbloggers and discussion topics. In: ICWSM

  29. Ferree M (2003) Women and the web: cybersex activity and implications. Sex Relationsh Ther 18(3):385–393

    Article  Google Scholar 

  30. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    MathSciNet  Article  Google Scholar 

  31. Gavin J, Rodham K, Poyer H (2008) The presentation of “pro-anorexia” in online group interactions. Qual Health Res 18(3):325–333

    Article  Google Scholar 

  32. Grabowicz PA, Aiello LM, Eguiluz VM, Jaimes A (2013) Distinguishing topical and social groups based on common identity and bond theory. In: WSDM. ACM

  33. Guerra PHC, Meira Jr, W, Cardie C, Kleinberg R (2013) A measure of polarization on social media networks based on community boundaries. In: ICWSM

  34. Haas SM, Irr ME, Jennings NA, Wagner LM (2010) Online negative enabling support groups. New Media & Society, Beverley Hills

    Google Scholar 

  35. Hald GM (2006) Gender differences in pornography consumption among young heterosexual danish adults. Arch Sex Behav 35(5):577–585

    Article  Google Scholar 

  36. Hald GM, Malamuth NN, Lange T (2013) Pornography and sexist attitudes among heterosexuals. J Commun 63(4):638–660

    Article  Google Scholar 

  37. Hald GM, Štulhofer A (2015) What types of pornography do people use and do they cluster? Assessing types and categories of pornography consumption in a large-scale online sample. J Sex Res 53(7):1–11

    Google Scholar 

  38. Hodas NO, Kooti F, Lerman K (2013) Friendship paradox redux: your friends are more interesting than you. In: ICWSM

  39. Kayes I, Kourtellis N, Quercia D, Iamnitchi A, Bonchi F (2015) The social world of content abusers in community question answering. In: WWW: proceedings of the 24th international conference on World Wide Web. ACM, New York, NY, USA, pp 570–580

  40. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  41. Kühn S, Gallinat J (2014) Brain structure and functional connectivity associated with pornography consumption: the brain on porn. JAMA Psychiatry 71(7):827–834

    Article  Google Scholar 

  42. Kvalem IL, Træen B, Lewin B, Štulhofer A (2014) Self-perceived effects of internet pornography use, genital appearance satisfaction, and sexual self-esteem among young Scandinavian adults. Cyberpsychol J Psychosoc Res Cyberspace 8(4):4. doi:10.5817/CP2014-4-4

    Google Scholar 

  43. Lee L-H, Chen H-H (2011) Collaborative cyberporn filtering with collective intelligence. In: SIGIR. ACM

  44. Lerman K, Yan X, Wu X-Z (2016) The “majority illusion” in social networks. PLoS One 11(2):1–13

    Article  Google Scholar 

  45. Leskovec J, Horvitz E (2008) Planetary-scale views on a large instant-messaging network. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 915–924

  46. Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: ACM SIGKDD. ACM

  47. Leskovec J, McGlohon M, Faloutsos C, Glance NS, Hurst M (2007) Patterns of cascading behavior in large blog graphs. In: SDM, vol 7. SIAM, pp 551–556

  48. Martin-Borregon D, Aiello LM, Grabowicz P, Jaimes A, Baeza-Yates R (2014) Characterization of online groups along space, time, and social dimensions. EPJ Data Sci 3(1):8

    Article  Google Scholar 

  49. Mitchell KJ, Finkelhor D, Wolak J (2003) The exposure of youth to unwanted sexual material on the internet a national survey of risk, impact, and prevention. Youth Soc 34(3):330–358

    Article  Google Scholar 

  50. Morgan EM, Snelson C, Elison-Bowers P (2010) Image and video disclosure of substance use on social media websites. Comput Hum Behav 26(6):1405–1411

    Article  Google Scholar 

  51. Negoescu RA, Gatica-Perez D (2008) Analyzing flickr groups. In: CIVR. ACM, New York, NY, USA

  52. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582

    Article  Google Scholar 

  53. Nicosia V, Latora V (2015) Measuring and modeling correlations in multiplex networks. Phys Rev E 92(3):032805

    Article  Google Scholar 

  54. Pastor-Satorras R, Vespignani A (2002) Epidemics and immunization in scale-free networks. In: Bornholdt S, Schuster HG (eds) Handbook of graphs and networks: from the genome to the internet. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

    Google Scholar 

  55. Phillips DJ (1996) Defending the boundaries: identifying and countering threats in a usenet newsgroup. Inf Soc 12(1):39–62

    Article  Google Scholar 

  56. Pingdom (2012) Report: social network demographics in 2012. In: Pingdom.com Tech Blog

  57. Ramos JdS, Pereira Neto AdF, Bagrichevsky M (2011) Pro-anorexia cultural identity: characteristics of a lifestyle in a virtual community. Interface (Botucatu) 15(37):447–460

    Article  Google Scholar 

  58. Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011) Detecting and tracking the spread of astroturf memes in microblog streams. In: WWW

  59. Romero DM, Tan C, Ugander J (2013) On the interplay between social and topical structure. In: ICWSM

  60. Romito P, Beltramini L (2015) Factors associated with exposure to violent or degrading pornography among high school students. J Sch Nurs 31(4):280–290

    Article  Google Scholar 

  61. Sabina C, Wolak J, Finkelhor D (2008) The nature and dynamics of internet pornography exposure for youth. Cyberpshychol Behav 11(6):691–693

    Article  Google Scholar 

  62. Schifanella R, Barrat A, Cattuto C, Markines B, Menczer F (2010) Folks in folksonomies: social link prediction from shared metadata. In: WSDM. ACM

  63. Schuhmacher M, Zirn C, Völker J (2013) Exploring youporn categories, tags, and nicknames for pleasant recommendations. In: Workshop on search and exploration of X-rated information. ACM

  64. Shores KB, He Y, Swanenburg KL, Kraut R, Riedl J (2014) The identification of deviance and its impact on retention in a multiplayer game. In: CSCW: proceedings of the 17th ACM conference on computer supported cooperative work and social computing. ACM, New York, NY, USA, pp 1356–1365

  65. Suler JR, Phillips WL (1998) The bad boys of cyberspace: deviant behavior in a multimedia chat community. Cyberpsychol Behav 1(3):275–294

    Article  Google Scholar 

  66. Thibaut JW, Kelley HH (1959) The social psychology of groups. In: Database: PsycINFO

  67. Tyson G, Elkhatib Y, Sastry N, Uhlig S (2013) Demystifying porn 2.0: a look into a major adult video streaming website. In: IMC. ACM

  68. Tyson G, Elkhatib Y, Sastry N, Uhlig S (2015) Are people really social in porn 2.0? In: ICWSM

  69. Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, Cambridge

    Google Scholar 

  70. Wellen JM, Neale M (2006) Deviance, self-typicality, and group cohesion the corrosive effects of the bad apples on the barrel. Small Group Res 37(2):165–186

    Article  Google Scholar 

  71. Wolak J, Mitchell K, Finkelhor D (2007) Unwanted and wanted exposure to online pornography in a national sample of youth internet users. Pediatrics 119(2):247–257

    Article  Google Scholar 

  72. Xu J, Chen H (2008) The topology of dark networks. Commun ACM 51(10):58–65

    Article  Google Scholar 

  73. Ybarra ML, Mitchell KJ (2005) Exposure to internet pornography among children and adolescents: a national survey. Cyberpsychol Behav 8(5):473–486

    Article  Google Scholar 

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Correspondence to Mauro Coletto.

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Coletto, M., Aiello, L.M., Lucchese, C. et al. Adult content consumption in online social networks. Soc. Netw. Anal. Min. 7, 28 (2017). https://doi.org/10.1007/s13278-017-0449-y

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Keywords

  • Deviant network
  • Deviant behavior
  • Pornography
  • Adult content consumption
  • Sexual content production
  • Social media
  • Online social network
  • Tumblr
  • Flickr