Abstract
Facebook users unknowingly reveal personal information that may help attackers to perpetrate malicious actions. In this paper, we show how sensitive age information of a given target user can be predicted from his/her online pictures. More precisely, we perform age inference attacks by leveraging picture metadata such as (i) alt-texts automatically generated by Facebook to describe the picture content, and (ii) picture reactions (comments and emojis) of other Facebook users. We investigate whether the target’s age affects other users’ reactions to his/her pictures. Our experiments show that age information can be inferred with AUC of 62% by using only alt-texts and with AUC of 89% by using combination of alt-texts and users’ reactions. Additionally, we present a detailed analysis of spearman correlation between reactions of Facebook users and age.
This work is supported by DIGITRUST (http://lue.univ-lorraine.fr/fr/article/digitrust/).
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Abdelberi, C., Ács, G., Kâafar, M.A.: You are what you like! information leakage through users’ interests. In: 19th Annual Network and Distributed System Security Symposium, NDSS 2012, San Diego, California, USA, 5–8 February 2012. The Internet Society (2012)
Alipour, B., Imine, A., Rusinowitch, M.: Gender inference for Facebook picture owners. In: Gritzalis, S., Weippl, E.R., Katsikas, S.K., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) TrustBus 2019. LNCS, vol. 11711, pp. 145–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27813-7_10
Alter, A.L., Hershfield, H.E.: People search for meaning when they approach a new decade in chronological age. Proc. Nat. Acad. Sci. 111(48), 17066–17070 (2014)
Belinic, T.: Personality profile of social media users how to get maximum from it. https://medium.com/krakensystems-blog/personality-profile-of-social-media-users-how-to-get-maximum-from-it-5e8b803efb30 April 2009
Clement, J.: Distribution of facebook users worldwide as of January 2020, by age and gender, February 2020
Cox, D.R.: The regression analysis of binary sequences. J. Royal Stat. Soc.: Ser. B (Methodological) 20(2), 215–232 (1958)
Dey, R., Tang, C., Ross, K.W., Saxena, N.: Estimating age privacy leakage in online social networks. In: Proceedings of the IEEE INFOCOM 2012, Orlando, FL, USA, 25–30 March 2012, pp. 2836–2840. IEEE (2012)
Farahbakhsh, R., Han, X., Cuevas, A., Crespi, N.: Analysis of publicly disclosed information in facebook profiles. In: Advances in Social Networks Analysis and Mining 2013, ASONAM 2013, Niagara, ON, Canada - 25–29 August 2013, pp. 699–705. ACM (2013)
Gong, N.Z., Bin L.: You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors. CoRR, abs/1606.05893:979–995 (2016)
Kellogg, K.: The 7 biggest social media sites in 2020. https://www.searchenginejournal.com/social-media/biggest-social-media-sites/ February 2020
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Nat. Acad. Sci. 110(15), 5802–5805 (2013)
Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: How old do you think I am? a study of language and age in twitter. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, 8–11 July 2013. The AAAI Press (2013)
Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In Proceedings of the 5th ACL Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, LaTeCH@ACL 2011, 24 June, 2011, Portland, Oregon, USA, pp. 115–123. The Association for Computer Linguistics (2011)
Pennacchiotti, M., Popescu, A.-M.: A machine learning approach to twitter user classification. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 17–21 July 2011. The AAAI Press (2011)
Pennebaker, J.W., Stone, L.D.: Words of wisdom: language use over the life span. J. Personality Soc. Psychol. 85(2), 291 (2003)
Pijani, B.A., Imine, A., Rusinowitch, M.: You are what emojis say about your pictures: language-independent gender inference attack on facebook. In SAC 2020: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020, pp. 1826–1834. ACM (2020)
Rangel, F., Rosso, P.: Use of language and author profiling: identification of gender and age. Nat. Lang. Process. Cognitive Sci. 1, 177 (2013)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents, SMUC@CIKM 2010, Toronto, ON, Canada, October 30, 2010, pp. 37–44. ACM (2010)
Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 1135–1144. ACM (2016)
Sung, Y., Lee, J.-A., Kim, E., Choi, S.M.: Understanding motivations for postingpictures of oneself: why we post selfies. Personal. Individual Diff. 97, 260–265 (2016)
You, Q., Bhatia, S., Luo, J.: A picture tells a thousand words - about you! user interest profiling from user generated visual content. Signal Process. 124, 45–53 (2016)
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Eidizadehakhcheloo, S., Pijani, B.A., Imine, A., Rusinowitch, M. (2020). Your Age Revealed by Facebook Picture Metadata. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_22
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