Abstract
In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied to the Russian online social network VKontakte. We gathered user profile data from most popular communities about suicide and depression on VK.com and performed comparative analysis between them and randomly sampled users. We have used not only standard user attributes like age, gender, or number of friends but also structural properties of their egocentric networks, with results similar to the study of suicide propensity in the Japanese social network Mixi.com. Our goal is to test the approach and models in this new setting and propose enhancements to the research design and analysis. We investigate the resulting classifiers to identify profile features that can indicate depression propensity of the users in order to provide tools for early depression detection. Finally, we discuss further work that might improve our analysis and transfer the results to practical applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., Liu, B.: Predicting flu trends using twitter data. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 702–707. IEEE (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)
Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market, October 2010. http://arxiv.org/abs/1010.3003
Choudhury, M.D., Counts, S., Horvitz, E., Hoff, A.: Characterizing and predicting postpartum depression from shared facebook data. In: Computer Supported Cooperative Work, CSCW 2014, Baltimore, MD, USA, 15–19 February, pp. 626–638 (2014). http://doi.acm.org/10.1145/2531602.2531675
Choudhury, M.D., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, 8–11 July 2013. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6124
Gnatyshak, D., Ignatov, D.I., Semenov, A., Poelmans, J.: Gaining insight in social networks with biclustering and triclustering. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds.) BIR 2012. LNBIP, vol. 128, pp. 162–171. Springer, Heidelberg (2012)
Holland, P.W., Leinhardt, S.: Transitivity in structural models of small groups. Comp. Group Stud. 2, 107–124 (1971)
Ling, C.X., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. In: Proceedings of the International Joint Conference on Artificial Intelligence 2003, pp. 519–526 (2003)
Masuda, N., Kurahashi, I., Onari, H.: Suicide ideation of individuals in online social networks. PLoS ONE 8(4), e62262 (2013). http://dx.doi.org/10.1371%2Fjournal.pone.0062262
Porshnev, A., Redkin, I.: Analysis of twitter users’ mood for prediction of gold and silver prices in the stock market. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorsky, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 190–197. Springer, Heidelberg (2014)
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics. In: Proceedings of the 20th International Conference on World Wide Web - WWW 2011, p. 695. ACM Press, New York (2011). http://portal.acm.org/citation.cfm?doid=1963405.1963503
Rosenquist, J.N., Fowler, J.H., Christakis, N.A.: Social network determinants of depression. Mol. Psychiatry 16(3), 273–281 (2011)
Vorontsov, K., Potapenko, A.: Tutorial on probabilistic topic modeling: additive regularization for stochastic matrix factorization. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorsky, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 29–46. Springer, Heidelberg (2014)
Yu, S., Kak, S.: A survey of prediction using social media. arXiv preprint arXiv:1203.1647 (2012)
Zimmer, M.: “But the data is already public”: on the ethics of research in Facebook. Ethics Inf. Technol. 12(4), 313–325 (2010). http://www.springerlink.com/index/10.1007/s10676-010-9227-5
Acknowledgements
The work of Sergey Nikolenko was supported by the Basic Research Program of the National Research University Higher School of Economics, 2015, grant No 78.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Semenov, A., Natekin, A., Nikolenko, S., Upravitelev, P., Trofimov, M., Kharchenko, M. (2015). Discerning Depression Propensity Among Participants of Suicide and Depression-Related Groups of Vk.com. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-26123-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26122-5
Online ISBN: 978-3-319-26123-2
eBook Packages: Computer ScienceComputer Science (R0)