Online Social Media Communication: the Effect of Having Privacy Violation Experience on Online Behavior

Objective. To analyze the effect of privacy violation experience on privacy-protective behaviorsBackground. In the era of rapid development of Internet technologies, privacy issues call for scientific reflection. Understanding the factors that regulate online user behavior might assist in elaborating the adequate privacy policy.Study design. Regression analysis provides a parametric evaluation of the effect of privacy experience on usage of privacy settings. Various matching technics were applied for preliminary balancing of the control (N=215) and treatment groups (N=160) by a set of key covariates.Participants. Users of the largest Russian online social network VKontakte from the Russian city Vologda. The sample size is 375 respondents (55% female) from 16 to 83 age (Mean=32,5; Med.=31; SD=12,9).Measurements. Both survey data on privacy experience and observed behavioral data on privacy settings from users’ online accounts were used. Additionally, the scale of P. Totterdell & D. Holman on propensity to make social connection and M. Rosenberg’s self-esteem scale were adopted in the studyResults. The experience of privacy violation does not lead to the cautious behavior online: the users tend to regulate only the access to the public posts on profile due to past bad experience. The privacy settings literacy turns significantly affect the usage of privacy settings.Conclusions. The findings support the “privacy paradox” hypothesis. As having specific online privacy management skills encourages more cautious behavior online, digital literacy interventions can improve the safety of social networking sites.

1. Bezopasnost’ lichnykh dannykh [Elektronnyi resurs] [Security of personal data]. Levada-tsentr [Levada Center]. URL: www.levada.ru/2017/05/25/bezopasnost-personalnyh-dannyh/ (Accessed 21.09.2020). (In Russ.).
2. Devyatko I.F. Onlain issledovaniya i metodologiya sotsial’nykh nauk: novye gorizonty, novye (i ne stol’ novye) trudnosti [Online Research and Social Science Methodology: New Horizons, New (and Not So New) Challenges]. In Shashkina A.V. (eds.) Onlain issledovaniya v Rossi 2.0: sb. statei [Online research in Russia 2.0.]. Moscow: Publ. Severo-Vostok, 2010, pp. 17—30. (In Russ.).
3. Enikolopov R. Otsenivanie effekta vozdeistviya [The estimation of average treatment effect]. Kvantil’ = Quantile, 2009, no. 6, pp. 3—15. (In Russ.).
4. Internet v Rossii: dinamika proniknoveniya. Zima 2017—2018 [Elektronnyi resurs] [Internet in Russia: dynamics of penetration. Winter 2017-2018]. Fond obshchestvennogo mneniya [Public Opinion Fund]. URL: https://fom.ru/SMI-i-internet/13999 (Accessed 20.09.2020). (In Russ.).
5. Kompleksnyi analiz sostoyaniya prestupnosti v Rossiiskoi Federatsii i raschetnye varianty ee razvitiya [Elektronnyi resurs] [Comprehensive analysis of the state of crime in the Russian Federation and estimated options for its development]. Yu.M. Antonyan [i dr.]. URL: https://mvd. ru/upload/site163/document_text/Kompleksnyy_analiz__original-maket_24_04.pdf (Accessed 21.09.2020). (In Russ.).
6. Regiony Rossii. Sotsial’no-ekonomicheskie pokazateli [Regions of Russia. Socio-economic indicators]. Moscow: Federal State Statistics Service, 2017, no. 32, p. 1402. (In Russ.).
7. Sotsial’nye seti v Rossii: tsifry i trendy, osen’ 2019 [Elektronnyi resurs] [Social networks in Russia: numbers and trends, Fall 2019]. Brand Analytics. URL: https://br-analytics.ru/blog/social-media-russia-2019/ (Accessed 21.09.2020). (In Russ.).
8. Barnes S.B. A Privacy Paradox: Social Networking in the Unites States [Elektronnyi resurs]. First Monday, 2006. Vol. 11(9). URL: https://firstmonday.org/ojs/index.php/fm/article/ view/1394 (Accessed 09.08.2020).
9. Bozdogan H. Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 1987. Vol. 52(3), pp. 345—370.
10. Brown T.A. Confirmatory Factor Analysis for Applied Research. NY: Guilford, 1 st.ed., 2008. 462 p.
11. Burgoon J.K. Privacy and Communication. In: Communication Yearbook. Burgoon M. (Ed.). CA.: Sage, 1982, pp. 206—249.
12. Chen B., Marcus J. Students’ self-presentation on Facebook: An examination of personality and self-construal factors. Computers in Human Behavior, 2012. Vol. 28(6), pp. 2091—2099.
13. Christofides E., Muise A., Desmarais S. Hey mom, what’s on your Facebook? Comparing Facebook disclosure and privacy in adolescents and adults. Social Psychological and Personality Science, 2012. Vol. 3(1), pp. 48—54.
14. Christofides E., Muise A., Desmarais S. Risky disclosures on Facebook: The effect of having a bad experience on online behavior. Journal of adolescent research, 2012. Vol. 27(6), pp. 714—731.
15. Dienlin T., Trepte S. Is the privacy paradox a relic of the past? An in-depth analysis of privacy attitudes and privacy behaviors. European journal of social psychology, 2015. Vol. 45(3), pp. 285— 297.
16. Dienlin T., Metzger M.J. An extended privacy calculus model for SNSs: Analyzing self-disclosure and self-withdrawal in a representative US sample. Journal of Computer-Mediated Communication, 2016. Vol. 21(5), p. 368—383.
17. Donath J.S., Boyd D. Public displays of connection. BT technology Journa, 2004. Vol. 22(4), pp. 71—82.
18. Eurobarometer. Attitudes on Data Protection and Electronic Identity in the European Union. Brussels: European Commission. 2010 [Elektronnyi resurs]. URL: http://ec.europa.eu/public_ opinion/archives/ebs/ebs_335_en.pdf (Accessed 21.09.2020).
19. Federal Trade Commission. FTC Imposes $5 Billion Penalty and Sweeping New Privacy Restrictions on Facebook [Elektronnyi resurs]. URL: www.ftc.gov/news-events/press-releases/2019/07/ftc-imposes-5-billion-penalty-sweeping-new-privacy-restrictions (Accessed 20.09.2020).
20. Gerber N., Gerber P., Volkamer M. Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior. Computers and Security, 2018. Vol. 77, pp. 226—261. DOI:10.1016/j.cose.2018.04.002
21. Greifer N. Matching methods. 2020 [Elektronnyi resurs]. URL: https://cran.r-project.org/ web/packages/MatchIt/vignettes/matching-methods.html#choosing-a-matching-method/ (Accessed 02.02.2021).
22. Gu X.S., Rosenbaum P.R. Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 1993. Vol. 2(4), pp. 405—420.
23. Hernán M.A., Robins J.M. Causal Inference: What if. Boca Raton: Chapman & Hall/CRC, 1 st.ed., 2020. 302 p.
24. Hosmer D.W., Lemeshow S. Applied Logistic Regression. New York: Wiley, 3 st.ed., 2013. 528 p.
25. Iacus S.M., King G., Porro G. Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association, 2011. Vol. 106(493), pp. 345—361.
26. Iacus S.M., King G., Porro G. Causal inference without balance checking: Coarsened exact matching. Political analysis, 2012, pp. 1—24.
27. Ibrahim Y. The new risk communities: Social networking sites and risk. International. Journal of Media and Cultural Politics, 2008. Vol. 4(2), pp. 245—253. DOI:10.1386/macp.4.2.245_
28. Kalish Y., Robins G. Psychological predispositions and network structure: The relationship between individual predispositions, structural holes and network closure. Social Networks, 2006. Vol. 28, pp. 56—84. DOI:10.1016/j.socnet.2005.04.004
29. Kokolakis S. Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers and Security, 2017. Vol. 64, pp. 122—134. DOI:10.1016/j. cose.2015.07.002
30. LaPiere R.T. Attitudes vs. actions. Social forces, 1934. Vol. 13(2), pp. 230—237.
31. Lee E., Kim Y.J., Ahn J. How do people use Facebook features to manage social capital? Computers in Human Behavior, 2014. Vol. 36, pp. 440—445.
32. Liu D., Ainsworth S.E., Baumeister R.F. A meta-analysis of social networking online and social capital. Review of general psychology, 2016. Vol. 20(4), pp. 369—391. DOI:10.1037/gpr0000091
33. Louviere J.J., Hensher D.A., Swait J.D. Stated choice methods: analysis and applications. Cambridge University press, 1 st.ed., 2000. 402 p.
34. Nagelkerke N. A note on a general definition of the coefficient of determination. Biometrika, 1991. Vol. 78, pp. 691—692.
35. Newell P.B. Perspectives on Privacy. Journal of Environmental Psychology, 1995. Vol. 15, pp. 87—104.
36. Norris F.H., Smith T., Kaniasty K. Revisiting the Experience—Behavior Hypothesis: The Effects of Hurricane Hugo on Hazard Preparedness and Other Self-Protective Acts. Basic and Applied Social Psychology, 1999. Vol. 21(1), pp. 37—47. DOI:10.1207/s15324834basp2101_4
37. Park Y.J. Digital literacy and privacy behavior online. Communication Research, 2013. Vol. 40(2), pp. 215—236.
38. Park Y.J. Do men and women differ in privacy? Gendered privacy and (in)equality in the Internet. Computers in Human Behavior, 2015. Vol. 50, pp. 252—258. DOI:10.1016/j.chb.2015.04.011
39. Reeve B.B., Fayers P. Applying item response theory modeling for evaluating questionnaire item and scale properties. Assessing quality of life in clinical trials: methods of practice, 2005. Vol. 2, pp. 55—73.
40. Robins R.W., Hendin H.M., Trzesniewski K.H. Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and social psychology bulletin, 2001. Vol. 27(2), pp. 151—161.
41. Rosenberg M. Society and the adolescent self-image. Princeton, New Jersey: Princeton University Press, 1965. 338 p.
42. Rubin D.B. Bias Reduction Using Mahalanobis-Metric Matching. Biometrics, 1980. Vol. 36(2), pp. 293—298. DOI:10.2307/2529981
43. Rubin D.B. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 1974. Vol. 66, pp. 688—701.
44. Rubin D.B. Matching to remove bias in observational studies. Biometrics, 1973. Vol. 29, pp. 159—183.
45. Rykov Y., Koltsova O., Sinyavskaya Y. Effects of user behaviors on accumulation of social capital in an online social network. Plos one, 2020. Vol. 15(4). DOI:e0231837
46. Stuart E.A. Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, 2010. Vol. 25(1), pp. 1—21.
47. Stutzman F., Vitak J., Ellison N.B., Gray R., Lampe C. Privacy in interaction: Exploring disclosure and social capital in Facebook. In: Sixth international AAAI conference on weblogs and social media (Dublin, Ireland, June 4—7, 2012). Palo Alto: AAAI Press, 2012. Vol. 6(1).
48. Tavakol M., Dennick R. Making sense of Cronbach’s alpha. International Journal of Medical Education, 2011. Vol. 2, pp. 53—55. DOI:10.5116/ijme.4dfb.8dfd
49. Totterdell P., Holman D., Hukin A. Social networkers: Measuring and examining individual differences in propensity to connect with others. Social Networks, 2008. Vol. 30, pp. 283—296. DOI:10.1016/j.socnet.2008.04.003
50. Trepte S., Dienlin T., Reinecke L. Risky behaviors: How online experiences influence privacy behaviors. From the Gutenberg galaxy to the Google galaxy. In Stark B., Quiring O., Jackob N. (Ed.). Germany: UVK Verlag, 2014. 370 p.
51. Weinstein N. Effects of personal experience on self-protective behavior. Psychological Bulletin, 1989. Vol. 105, pp. 31—50.
52. Zakrison T.L., Austin P.C., McCredie V.A. A systematic review of propensity score methods in the acute care surgery literature: avoiding the pitfalls and proposing a set of reporting guidelines. European Journal of Trauma and Emergency Surgery, 2018. Vol. 44(3), pp. 385—395.