Writer Profiling Without the Writer’s Text

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)

Abstract

Social network users may wish to preserve their anonymity online by masking their identity and not using language associated with any particular demographics or personality. However, they have no control over the language in incoming communications. We show that linguistic cues in public comments directed at a user are sufficient for an accurate inference of that user’s gender, age, religion, diet, and even personality traits. Moreover, we show that directed communication is even more predictive of a user’s profile than the user’s own language. We then conduct a nuanced analysis of what types of social relationships are most predictive of users’ attributes, and propose new strategies on how individuals can modulate their online social relationships and incoming communications to preserve their anonymity.

References

  1. 1.
    Al Zamal, F., Liu, W., Ruths, D.: Homophily and latent attribute inference: inferring latent attributes of Twitter users from neighbors. In: Proceedings of ICWSM (2012)Google Scholar
  2. 2.
    Almishari, M., Oguz, E., Tsudik, G.: Fighting authorship linkability with crowdsourcing. In: Proceedings of COSN, pp. 69–82. ACM (2014)Google Scholar
  3. 3.
    Altenburger, K.M., Ugander, J.: Bias and variance in the social structure of gender. arXiv preprint arXiv:1705.04774 (2017)
  4. 4.
    Anderson, C., John, O.P., Keltner, D., Kring, A.M.: Who attains social status? effects of personality and physical attractiveness in social groups. J. Pers. Soc. Psychol. 81(1), 116 (2001)CrossRefGoogle Scholar
  5. 5.
    Baker, W., Bowie, D.: Religious affiliation as a correlate of linguistic behavior. Univ. Pennsylvania Work. Pap. Linguist. 15(2), 2 (2010)Google Scholar
  6. 6.
    Bamman, D., Eisenstein, J., Schnoebelen, T.: Gender identity and lexical variation in social media. J. Sociolinguist. 18(2), 135–160 (2014)CrossRefGoogle Scholar
  7. 7.
    Barbieri, F.: Patterns of age-based linguistic variation in American English. J. Sociolinguist. 12(1), 58–88 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Beller, C., Knowles, R., Harman, C., Bergsma, S., Mitchell, M., Van Durme, B.: I’m a belieber: social roles via self-identification and conceptual attributes. In: Proceedings of ACL, pp. 181–186 (2014)Google Scholar
  9. 9.
    Benton, A., Mitchell, M., Hovy, D.: Multitask learning for mental health conditions with limited social media data. In: Proceedings of EACL (2017)Google Scholar
  10. 10.
    Bergsma, S., Van Durme, B.: Using conceptual class attributes to characterize social media users. In: Proceedings of ACL (2013)Google Scholar
  11. 11.
    Best, P., Manktelow, R., Taylor, B.: Online communication, social media and adolescent wellbeing: a systematic narrative review. Child Youth Serv. Rev. 41, 27–36 (2014)CrossRefGoogle Scholar
  12. 12.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. (JMLR) 3, 993–1022 (2003)MATHGoogle Scholar
  13. 13.
    Bogardus, E.S.: A social distance scale. Sociol. Soc. Res. 17, 265–271 (1933)Google Scholar
  14. 14.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefMATHGoogle Scholar
  15. 15.
    Brennan, M., Afroz, S., Greenstadt, R.: Adversarial stylometry: circumventing authorship recognition to preserve privacy and anonymity. ACM Trans. Inf. Syst. Secur. (TISSEC) 15(3), 12 (2012)CrossRefGoogle Scholar
  16. 16.
    Brysbaert, M., Warriner, A.B., Kuperman, V.: Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46(3), 904–911 (2014)CrossRefGoogle Scholar
  17. 17.
    Bucholtz, M., Hall, K.: Identity and interaction: a sociocultural linguistic approach. Discourse Stud. 7(4–5), 585–614 (2005)CrossRefGoogle Scholar
  18. 18.
    Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on Twitter. In: Proceedings of EMNLP, pp. 1301–1309 (2011)Google Scholar
  19. 19.
    Carpenter, J., Preotiuc-Pietro, D., Flekova, L., Giorgi, S., Hagan, C., Kern, M.L., Buffone, A.E., Ungar, L., Seligman, M.E.: Real men don’t say “cute” using automatic language analysis to isolate inaccurate aspects of stereotypes. Soc. Psychol. Pers. Sci. 8, 310–322 (2016)CrossRefGoogle Scholar
  20. 20.
    Cesare, N., Grant, C., Nsoesie, E.O.: Detection of user demographics on social media: a review of methods and recommendations for best practices. arXiv preprint arXiv:1702.01807 (2017)
  21. 21.
    Chen, L., Weber, I., Okulicz-Kozaryn, A.: U.S. religious landscape on Twitter. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 544–560. Springer, Cham (2014). doi:10.1007/978-3-319-13734-6_38 Google Scholar
  22. 22.
    Chen, X., Wang, Y., Agichtein, E., Wang, F.: A comparative study of demographic attribute inference in Twitter. In: Proceedings of ICWSM, vol. 15, pp. 590–593 (2015)Google Scholar
  23. 23.
    Ciot, M., Sonderegger, M., Ruths, D.: Gender inference of Twitter users in non-English contexts. In: Proceedings of EMNLP, pp. 1136–1145 (2013)Google Scholar
  24. 24.
    Coates, J.: Language and Gender: A Reader. Wiley-Blackwell, Oxford (1998)Google Scholar
  25. 25.
    Coates, J.: Women, Men and Language: A Sociolinguistic Account of Gender Differences in Language. Routledge, Abingdon (2015)Google Scholar
  26. 26.
    Danescu-Niculescu-Mizil, C., Gamon, M., Dumais, S.: Mark my words!: linguistic style accommodation in social media. In: Proceedings of WWW, pp. 745–754. ACM (2011)Google Scholar
  27. 27.
    De Choudhury, M., De, S.: Mental health discourse on reddit: self-disclosure, social support, and anonymity. In: Proceedings of ICWSM (2014)Google Scholar
  28. 28.
    De Choudhury, M., Kiciman, E.: The language of social support in social media and its effect on suicidal ideation risk. In: Proceedings of ICWSM, pp. 32–41 (2017)Google Scholar
  29. 29.
    Derlega, V.J., Harris, M.S., Chaikin, A.L.: Self-disclosure reciprocity, liking and the deviant. J. Exp. Soc. Psychol. 9(4), 277–284 (1973)CrossRefGoogle Scholar
  30. 30.
    Dewaele, J.M.: Individual differences in the use of colloquial vocabulary: the effects of sociobiographical and psychological factors. In: Learning Vocabulary in a Second Language: Selection, Acquisition and Testing, pp. 127–153 (2004)Google Scholar
  31. 31.
    Duggan, M.: Mobile messaging and social media 2015. Pew Res. Center, 13 (2015)Google Scholar
  32. 32.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79228-4_1 CrossRefGoogle Scholar
  33. 33.
    Eagly, A.H., Mladinic, A.: Gender stereotypes and attitudes toward women and men. Pers. Soc. Psychol. Bull. 15(4), 543–558 (1989)CrossRefGoogle Scholar
  34. 34.
    Eckert, P.: Jocks and Burnouts: Social Categories and Identity in the High School. Teachers College Press, New York (1989)Google Scholar
  35. 35.
    Eckert, P.: Age as a sociolinguistic variable. In: The Handbook of Sociolinguistics, pp. 151–167 (1997)Google Scholar
  36. 36.
    Eckert, P.: Variation and the indexical field. J. Sociolinguist. 12(4), 453–476 (2008)CrossRefGoogle Scholar
  37. 37.
    Eckert, P., McConnell-Ginet, S.: Language and Gender. Cambridge University Press, New York (2003)CrossRefGoogle Scholar
  38. 38.
    El-Arini, K., Paquet, U., Herbrich, R., Van Gael, J., Agüera y Arcas, B.: Transparent user models for personalization. In: Proceedings of KDD, pp. 678–686. ACM (2012)Google Scholar
  39. 39.
    Elgin, B., Robison, P.: How despots use Twitter to hunt dissidents. BloombergBusinessweek (2016). https://www.bloomberg.com/news/articles/2016-10-27/twitter-s-firehose-of-tweets-is-incredibly-valuable-and-just-as-dangerous
  40. 40.
    Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res 15(1), 3133–3181 (2014)MathSciNetMATHGoogle Scholar
  41. 41.
    Flekova, L., Gurevych, I.: Can we hide in the web? large scale simultaneous age and gender author profiling in social media. In: Proceedings of CLEF (2013)Google Scholar
  42. 42.
    Friedkin, N.: A test of structural features of Granovetter’s strength of weak ties theory. Soc. Netw. 2(4), 411–422 (1980)CrossRefGoogle Scholar
  43. 43.
    Garimella, A., Mihalcea, R.: Zooming in on gender differences in social media. In: Proceedings of the Workshop on Computational Modeling of Peoples Opinions, Personality, and Emotions in Social Media, pp. 1–10 (2016)Google Scholar
  44. 44.
    Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of CHI, pp. 211–220. ACM (2009)Google Scholar
  45. 45.
    Golbeck, J., Robles, C., Edmondson, M., Turner, K.: Predicting personality from Twitter. In: Proceedings of SocialCom, pp. 149–156. IEEE (2011)Google Scholar
  46. 46.
    Goldin, C., Rouse, C.: Orchestrating impartiality: the impact of “blind” auditions on female musicians. Technical report, National Bureau of Economic Research (1997)Google Scholar
  47. 47.
    Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers age and gender. In: Proceedings of ICWSM (2009)Google Scholar
  48. 48.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)CrossRefGoogle Scholar
  49. 49.
    Hovy, D., Søgaard, A.: Tagging performance correlates with author age. In: Proceedings of ACL, pp. 483–488 (2015)Google Scholar
  50. 50.
    Hovy, D., Spruit, S.L.: The social impact of natural language processing. In: Proceedings of ACL, vol. 2, pp. 591–598 (2016)Google Scholar
  51. 51.
    John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, vol. 2, pp. 102–138 (1999)Google Scholar
  52. 52.
    Kendall, S., Tannen, D., et al.: Gender and language in the workplace. In: Gender and Discourse, pp. 81–105. Sage, London (1997)Google Scholar
  53. 53.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Nat. Acad. Sci. (PNAS) 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  54. 54.
    Krackhardt, D., Nohria, N., Eccles, B.: The strength of strong ties. Netw. Knowl. Econ., 82 (2003)Google Scholar
  55. 55.
    Labov, W.: Sociolinguistic Patterns. University of Pennsylvania Press, Philadelphia (1972)Google Scholar
  56. 56.
    Lakoff, R.T., Bucholtz, M.: Language and Woman’s Place: Text and Commentaries, vol. 3. Oxford University Press, USA (2004)Google Scholar
  57. 57.
    Lea, M., Spears, R., de Groot, D.: Knowing me, knowing you: anonymity effects on social identity processes within groups. Pers. Soc. Psychol. Bull. 27(5), 526–537 (2001)CrossRefGoogle Scholar
  58. 58.
    Lin, N., Ensel, W.M., Vaughn, J.C.: Social resources and strength of ties: structural factors in occupational status attainment. Am. Sociol. Rev., 393–405 (1981)Google Scholar
  59. 59.
    Liviatan, I., Trope, Y., Liberman, N.: Interpersonal similarity as a social distance dimension: Implications for perception of others actions. J. Exp. Soc. Psychol. 44(5), 1256–1269 (2008)CrossRefGoogle Scholar
  60. 60.
    Lu, X., Ai, W., Liu, X., Li, Q., Wang, N., Huang, G., Mei, Q.: Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users. In: Proceedings of Ubicomp, pp. 770–780. ACM (2016)Google Scholar
  61. 61.
    Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. (JAIR) 30, 457–500 (2007)MATHGoogle Scholar
  62. 62.
    Marder, B., Joinson, A., Shankar, A., Thirlaway, K.: Strength matters: self-presentation to the strongest audience rather than lowest common denominator when faced with multiple audiences in social network sites. Comput. Hum. Behav. 61, 56–62 (2016)CrossRefGoogle Scholar
  63. 63.
    Marwick, A.E., Boyd, D.: I tweet honestly, i tweet passionately: Twitter users, context collapse, and the imagined audience. New Media Soc. 13(1), 114–133 (2011)CrossRefGoogle Scholar
  64. 64.
    McCandless, M.: Accuracy and performance of Google’s compact language detector. Blog post (2010)Google Scholar
  65. 65.
    McCrae, R.R., Costa, P.T.: Reinterpreting the Myers-Briggs type indicator from the perspective of the five-factor model of personality. J. Pers. 57(1), 17–40 (1989)CrossRefGoogle Scholar
  66. 66.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  67. 67.
    Milroy, J.: Linguistic variation and change: on the historical sociolinguistics of English. B. Blackwell (1992)Google Scholar
  68. 68.
    Minkus, T., Liu, K., Ross, K.W.: Children seen but not heard: when parents compromise children’s online privacy. In: Proceedings of WWW, pp. 776–786. ACM (2015)Google Scholar
  69. 69.
    Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Artif. Intell. 29(3), 436–465 (2013)MathSciNetGoogle Scholar
  70. 70.
    Monroe, B.L., Colaresi, M.P., Quinn, K.M.: Fightin’ words: lexical feature selection and evaluation for identifying the content of political conflict. Polit. Anal. 16(4), 372–403 (2008)CrossRefGoogle Scholar
  71. 71.
    Nakagawa, S., Schielzeth, H.: A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4(2), 133–142 (2013)CrossRefGoogle Scholar
  72. 72.
    Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In: Proceedings of the Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 115–123. Association for Computational Linguistics (2011)Google Scholar
  73. 73.
    Nguyen, D.P., Gravel, R., Trieschnigg, R., Meder, T.: “how old do you think I am?” a study of language and age in Twitter. In: Proceedings of ICWSM (2013)Google Scholar
  74. 74.
    Nguyen, D.P., Trieschnigg, R., Doğruöz, A.S., Gravel, R., Theune, M., Meder, T., de Jong, F.: Why gender and age prediction from tweets is hard: lessons from a crowdsourcing experiment. In: Proceedings of COLING (2014)Google Scholar
  75. 75.
    Nguyen, M.T., Lim, E.P.: On predicting religion labels in microblogging networks. In: Proceedings of SIGIR, pp. 1211–1214. ACM (2014)Google Scholar
  76. 76.
    Niederhoffer, K.G., Pennebaker, J.W.: Linguistic style matching in social interaction. J. Lang. Soc. Psychol. 21(4), 337–360 (2002)CrossRefGoogle Scholar
  77. 77.
    Oomen, I., Leenes, R.: Privacy risk perceptions and privacy protection strategies. In: de Leeuw, E., Fischer-Hübner, S., Tseng, J., Borking, J. (eds.) IDMAN 2007. TIFIP, vol. 261, pp. 121–138. Springer, Boston, MA (2008). doi:10.1007/978-0-387-77996-6_10 CrossRefGoogle Scholar
  78. 78.
    Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-Generated Contents, pp. 37–44. ACM (2011)Google Scholar
  79. 79.
    Pennacchiotti, M., Popescu, A.M.: A machine learning approach to Twitter user classification. In: Proceedings of ICWSM, pp. 281–288 (2011)Google Scholar
  80. 80.
    Pennebaker, J.W., Stone, L.D.: Words of wisdom: language use over the life span. J. Pers. Soc. Psychol. 85(2), 291 (2003)CrossRefGoogle Scholar
  81. 81.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP, vol. 14, pp. 1532–1543 (2014)Google Scholar
  82. 82.
    Phelan, C., Lampe, C., Resnick, P.: It’s creepy, but it doesn’t bother me. In: Proceedings of CHI, pp. 5240–5251. ACM (2016)Google Scholar
  83. 83.
    Plank, B., Hovy, D.: Personality traits on TwitterorHow to get 1,500 personality tests in a week. In: Proceedings of WASSA (2015)Google Scholar
  84. 84.
    Postmes, T., Spears, R., Lea, M.: Breaching or building social boundaries? SIDE-effects of computer-mediated communication. Commun. Res. 25(6), 689–715 (1998)CrossRefGoogle Scholar
  85. 85.
    Potthast, M., Hagen, M., Stein, B.: Author obfuscation: attacking the state of the art in authorship verification. In: Proceedings of CLEF (Working Notes), pp. 716–749 (2016)Google Scholar
  86. 86.
    Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our Twitter profiles, our selves: predicting personality with Twitter. In: Proceedings of SocialCom, pp. 180–185. IEEE (2011)Google Scholar
  87. 87.
    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, pp. 37–44. ACM (2010)Google Scholar
  88. 88.
    Reddy, S., Knight, K.: Obfuscating gender in social media writing. In: Proceedings of Workshop on Natural Language Processing and Computational Social Science, pp. 17–26 (2016)Google Scholar
  89. 89.
    Reed, P.J., Spiro, E.S., Butts, C.T.: Thumbs up for privacy?: differences in online self-disclosure behavior across national cultures. Soc. Sci. Res. 59, 155–170 (2016)CrossRefGoogle Scholar
  90. 90.
    Rosenthal, S., McKeown, K.: Age prediction in blogs: a study of style, content, and online behavior in pre-and post-social media generations. In: Proceedings of ACL, pp. 763–772. Association for Computational Linguistics (2011)Google Scholar
  91. 91.
    Rossi, L., Magnani, M.: Conversation practices and network structure in Twitter. In: Proceedings of ICWSM (2012)Google Scholar
  92. 92.
    Ryan, E.B., Hummert, M.L., Boich, L.H.: Communication predicaments of aging patronizing behavior toward older adults. J. Lang. Soc. Psychol. 14(1–2), 144–166 (1995)CrossRefGoogle Scholar
  93. 93.
    Sap, M., Park, G., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., Ungar, L., Schwartz, H.A.: Developing age and gender predictive lexica over social media. In: Proceedings of EMNLP, pp. 1146–1151. Association for Computational Linguistics (2014)Google Scholar
  94. 94.
    Schnoebelen, T.J.: Emotions are relational: positioning and the use of affective linguistic resources. Ph.D. thesis, Stanford University (2012)Google Scholar
  95. 95.
    Schrammel, J., Köffel, C., Tscheligi, M.: Personality traits, usage patterns and information disclosure in online communities. In: Proceedings of HCI, pp. 169–174. British Computer Society (2009)Google Scholar
  96. 96.
    Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E.S., Ungar, L.H.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9), e73791 (2013)CrossRefGoogle Scholar
  97. 97.
    Shelton, M., Lo, K., Nardi, B.: Online media forums as separate social lives: a qualitative study of disclosure within and beyond Reddit. In: Proceedings of iConference (2015)Google Scholar
  98. 98.
    Snefjella, B., Kuperman, V.: Concreteness and psychological distance in natural language use. Psychol. Sci. 26(9), 1449–1460 (2015)CrossRefGoogle Scholar
  99. 99.
    Soderberg, C., Callahan, S., Kochersberger, A., Amit, E., Ledgerwood, A.: The effects of psychological distance on abstraction: two meta-analyses. Psychol. Bull. 141(3), 525–548 (2015)CrossRefGoogle Scholar
  100. 100.
    Spears, R., Lea, M.: Social influence and the influence of the “social” in computer-mediated communication. In: Lea, M. (ed.) Contexts of Computer-Mediated Communication, pp. 30–65. Harvester Wheatsheaf (1992)Google Scholar
  101. 101.
    Steinpreis, R.E., Anders, K.A., Ritzke, D.: The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: a national empirical study. Sex Roles 41(7), 509–528 (1999)CrossRefGoogle Scholar
  102. 102.
    Strater, K., Lipford, H.R.: Strategies and struggles with privacy in an online social networking community. In: Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction, vol. 1, pp. 111–119. British Computer Society (2008)Google Scholar
  103. 103.
    Stutzman, F., Vitak, J., Ellison, N.B., Gray, R., Lampe, C.: Privacy in interaction: exploring disclosure and social capital in Facebook. In: Proceedings of ICWSM (2012)Google Scholar
  104. 104.
    Tannen, D.: You Just Don’t Understand: Women and Men in Conversation. Virago, London (1991)Google Scholar
  105. 105.
    Tannen, D.: Gender and Conversational Interaction. Oxford University Press, Oxford (1993)Google Scholar
  106. 106.
    Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRefGoogle Scholar
  107. 107.
    Tchokni, S.E., Séaghdha, D.O., Quercia, D.: Emoticons and phrases: status symbols in social media. In: Proceedings of ICWSM (2014)Google Scholar
  108. 108.
    Thomas, K., Grier, C., Nicol, D.M.: unFriendly: multi-party privacy risks in social networks. In: Atallah, M.J., Hopper, N.J. (eds.) PETS 2010. LNCS, vol. 6205, pp. 236–252. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14527-8_14 CrossRefGoogle Scholar
  109. 109.
    Trepte, S., Reinecke, L., Ellison, N.B., Quiring, O., Yao, M.Z., Ziegele, M.: A cross-cultural perspective on the privacy calculus. Soc. Media+ Soc. 3(1), 2056305116688035 (2017)Google Scholar
  110. 110.
    Trope, Y., Liberman, N.: Construal-level theory of psychological distance. Psychol. Rev. 117(2), 440 (2010)CrossRefGoogle Scholar
  111. 111.
    Volkova, S., Bachrach, Y., Armstrong, M., Sharma, V.: Inferring latent user properties from texts published in social media. In: Proceedings of AAAI, pp. 4296–4297 (2015)Google Scholar
  112. 112.
    Wienberg, C., Gordon, A.S.: Privacy considerations for public storytelling. In: Proceedings of ICWSM (2014)Google Scholar
  113. 113.
    Yaeger-Dror, M.: Religion as a sociolinguistic variable. Language and Linguistics Compass 8(11), 577–589 (2014)CrossRefGoogle Scholar
  114. 114.
    Youn, S., Hall, K.: Gender and online privacy among teens: risk perception, privacy concerns, and protection behaviors. Cyberpsychol. Behav. 11(6), 763–765 (2008)CrossRefGoogle Scholar
  115. 115.
    Zhang, K., Kizilcec, R.F.: Anonymity in social media: effects of content controversiality and social endorsement on sharing behavior. In: Proceedings of ICWSM (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

Personalised recommendations