Abstract
Every individual possess a set of Basic Human Values such as self-direction, power, and hedonism. These values drive an individual to commit actions in various situations in her daily lives. Values represent one’s attitudes, opinions, thoughts and goals in life, and can regulate a variety of human behaviors and manners that an individual shows in the society. In this paper, we identify five higher-level values from social media interactions by analyzing two types of contents: user generated and user supported. More importantly, we identify which type of content can better predict which human values in different scenarios, which ultimately helps us to predict human values for both silent and active users. We also build a combined value prediction model by integrating different types of interaction features, which can more accurately capture the human values than that of a single feature based model. We also build separate models for silent and active users of SNS to effectively predict values for different types of SNS users. Finally, we compare the strength of different types of models to predict values from social media usage effectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
For the sake of simplicity, we would refer UG and US for “user generated” and “user supported” respectively throughout the paper.
References
Back, M.D., Stopfer, J.M., Vazire, S., Gaddis, S., Schmukle, S.C., Egloff, B., Gosling, S.D.: Facebook profiles reflect actual personality, not self-idealization. Psychol. Sci. 21, 372 (2010)
Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: ICWSM 11, pp. 450–453 (2011)
Boyd, R.L., Wilson, S.R., Pennebaker, J.W., Kosinski, M., Stillwell, D.J., Mihalcea, R.: Values in words: using language to evaluate and understand personal values. In: ICWSM (2015)
Celli, F., Pianesi, F., Stillwell, D., Kosinski, M.: Workshop on computational personality recognition (shared task). In: Proceedings of the Workshop on Computational Personality Recognition (2013)
Chen, J., Hsieh, G., Mahmud, J.U., Nichols, J.: Understanding individuals’ personal values from social media word use. In: CSCW, pp. 405–414. ACM (2014)
Cohen, R., Ruths, D.: Classifying political orientation on twitter: it’s not easy!. In: ICWSM (2013)
Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951)
Derksen, S., Keselman, H.: Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British J. Math. Stat. Psychol. 45(2), 265–282 (1992)
Fast, E., Chen, B., Bernstein, M.: Empath: Understanding topic signals in large-scale text. arXiv preprint arXiv:1602.06979 (2016)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett., 861–874 (2006)
Golbeck, J., Hansen, D.: Computing political preference among twitter followers. In: Proceeding of CHI, pp. 1105–1108. ACM (2011)
Golbeck, J., Robles, C., Edmondson, M., Turner, K.: Predicting personality from twitter. In: SocialCom, pp. 149–156. IEEE (2011)
Gong, W., Lim, E.P., Zhu, F.: Characterizing silent users in social media communities. In: ICWSM (2015)
Hastie, T., Qian, J.: Glmnet vignette. Technical report, Stanford (2014)
Hsieh, G., Chen, J., Mahmud, J.U., Nichols, J.: You read what you value: understanding personal values and reading interests. In: CHI, pp. 983–986. ACM (2014)
Hughes, D.J., Rowe, M., Batey, M., Lee, A.: A tale of two sites: Twitter vs. facebook and the personality predictors of social media usage. Comput. Hum. Behav. 28(2), 561–569 (2012)
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. In: Proceeding of the National Academy of Sciences, pp. 5802–5805 (2013)
Kuhn, M.: Caret package. J. Stat. Softw. 28(5), 1–26 (2008)
Lumley, T., Miller, A.: Leaps: regression subset selection. r package version 2.9 (2009)
Marshall, M.N.: Sampling for qualitative research. Family Practice 13(6), 522–526 (1996)
Maruf, H.A., Mahmud, J., Ali, M.E.: Can hashtags bear the testimony of personality? Predicting personality from hashtag use (2014)
Maruf, H.A., Meshkat, N., Ali, M.E., Mahmud, J.: Human behaviour in different social medias: a case study of twitter and disqus. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 270–273. ACM (2015)
Oentaryo, R.J., Lim, E.P., Lo, D., Zhu, F., Prasetyo, P.K.: Collective churn prediction in social network. In: Proceeding of ASONAM 2012, pp. 210–214. IEEE Computer Society (2012)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. LREC 10, 1320–1326 (2010)
Pennebaker, J.W., Booth, R.J., Francis, M.E.: Linguistic inquiry and word count: Liwc [computer software]. liwc.net, Austin (2007)
Preece, J., Nonnecke, B., Andrews, D.: The top five reasons for lurking: improving community experiences for everyone. Comput. Hum. Behav. 20(2), 201–223 (2004)
Rahman, M.M., Majumder, M.T.H., Mukta, M.S.H., Ali, M.E., Mahmud, J.: Can we predict eat-out preference of a person from tweets? In: Proceedings of the 8th ACM Conference on Web Science, pp. 350–351. ACM (2016)
Schwartz, S.H.: A proposal for measuring value orientations across nations. In: Questionnaire Package of ESS, pp. 259–290 (2003)
Schwartz, S.H.: Basic human values: their content and structure across countries. In: Tamayo, A., Porto, J. (eds.) Valores e Trabalho [Values and Work], pp. 21–55. Editora Vozes, Brasilia (2005)
Schwartz, S.H., Melech, G., Lehmann, A., Burgess, S., Harris, M., Owens, V.: Extending the cross-cultural validity of the theory of basic human values with a different method of measurement. J. Cross Cult. Psychol. 32(5), 519–542 (2001)
Sill, J., Takács, G., Mackey, L., Lin, D.: Feature-weighted linear stacking. arXiv preprint arXiv:0911.0460 (2009)
Smith, A.: 6 new facts about Facebook (2014). http://www.pewresearch.org/fact-tank/2014/02/03/6-new-facts-about-facebook/
Sumner, C., Byers, A., Boochever, R., Park, G.J.: Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In: ICMLA. IEEE (2012)
Wiltfong, J.: Global Sharers on Social Media Sites (2013). http://www.ipsos-na.com/news-polls/pressrelease.aspx?id=6239
Wong, F.M.F., Tan, C.W., Sen, S., Chiang, M.: Quantifying political leaning from tweets and retweets. In: ICWSM (2013)
Yang, C., Lin, K.H.Y., Chen, H.H.: Building emotion lexicon from weblog corpora. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 133–136. Association for Computational Linguistics (2007)
Acknowledgement
This research is funded by ICT Division, Ministry of Posts, Telecommunications and Information Technology, Government of the People’s Republic of Bangladesh.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Mukta, M.S.H., Ali, M.E., Mahmud, J. (2016). User Generated vs. Supported Contents: Which One Can Better Predict Basic Human Values?. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-47874-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47873-9
Online ISBN: 978-3-319-47874-6
eBook Packages: Computer ScienceComputer Science (R0)