Towards suicide prevention: Early detection of depression on social media

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10673 LNCS, Issue , 2017, Pages 428-436

  Leiva, Victor ^a   Freire, Ana ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

Depression; Early detection; Machine learning; Social media

Indexed keywords

DISEASES; LEARNING ALGORITHMS; LEARNING SYSTEMS; RISK ASSESSMENT;

DEPRESSION; MENTAL ILLNESS; SENTIMENT ANALYSIS; SOCIAL MEDIA; TIME-PERIODS; WORLD HEALTH ORGANIZATION;

SOCIAL NETWORKING (ONLINE);

EID: 85033602297     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-70284-1_34     Document Type: Conference Paper

References (14)

1. Brubaker, J.R., Kivran-Swaine, F., Taber, L., Hayes, G.R.: Grief-stricken in a crowd: the language of bereavement and distress in social media. In: Proceedings of ICWSM (2012)
2. Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., Bierut, L.J., Cavazos-Rehg, P.A., Krauss, M.J.: A content analysis of depression-related tweets. Comput. Hum. Behav. 1(54), 351–357 (2016)
3. Cole, D.A., Zelkowitz, R.L., Nick, E., Martin, N.C., Roeder, K.M., Sinclair-McBride, K., Spinelli, T.: Longitudinal and incremental relation of cybervictimization to negative self-cognitions and depressive symptoms in young adolescents. J. Abnorm. Child Psychol. 44(7), 1321–1332 (2016)
4. De Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 47–56. ACM (2013)
5. Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Web and Social Media (2014)
6. Wills, G., Syarif, I., Prugel-Bennett, A.: SVM parameter optimization using grid search and genetic algorithm to improve classification performance. TELKOMNIKA 14(4), 1502–1509 (2016)
7. Losada, D.E., Crestani, F.: A test collection for research on depression and language use. In: Fuhr, N., Quaresma, P., Gonçalves, T., Larsen, B., Balog, K., Macdonald, C., Cappellato, L., Ferro, N. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 28–39. Springer, Cham (2016). doi:10.1007/978-3-319-44564-9_3
8. Nadeem, M.: Identifying depression on Twitter. arXiv preprint:1607.07384 (2016)
9. Park, M., Cha, C., Cha, M.: Depressive moods of users portrayed in Twitter. In: Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics, pp. 1–8 (2012)
10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
11. Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., Bao, Z.: A depression detection model based on sentiment analysis in micro-blog social network. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013. LNCS, vol. 7867, pp. 201–213. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40319-4_18
12. WHO. http://www.euro.who.int. Accessed 06 May 2017
13. WHO. Preventing Suicide: A Global Imperative (2015)
14. Srinivasan, P., Yang, C.: Life satisfaction and the pursuit of happiness on Twitter. PLoS ONE 11(3), e0150881 (2016)