Abstract
The abundance and growing usage of social media has given an unprecedented access to users’ social accounts for studying people’s thoughts and sentiments. In this work, we are interested in tracking individual’s emotional states and more specifically suicidal ideation in microblogging services. We propose a probabilistic framework that models user’s online activities as a sequence of psychological states over time and predicts the emotional states by incorporating the context history. Based on Conditional Random Fields, our model is able to provide comprehensive interpretations of the relationship between the risk factors and psychological states. We evaluated our approach within real case studies of Twitter’ users that have demonstrated a serious change in their emotional states and online behaviour. Our experiments show that the model is able to identify suicidal ideation with high precision and good recall with substantial improvements on state-of-the-art methods.
Notes
- 1.
- 2.
DARE stands for conDitionAl Random fiElds.
- 3.
- 4.
- 5.
- 6.
- 7.
References
Adler, A., Bush, A., Barg, F.K., Weissinger, G., Beck, A.T., Brown, G.K.: A mixed methods approach to identify cognitive warning signs for suicide attempts. Arch. Suicide Res. 20(4), 528–538 (2016)
Burnap, P., Colombo, W., Scourfield, J.: Machine classification and analysis of suicide-related communication on twitter. In: Proceedings of the 26th ACM Conference on Hypertext and Social Media, HT 2015, pp. 75–84. ACM, New York (2015)
De Choudhury, M., Counts, S., Horvitz, E.: Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, pp. 3267–3276. ACM, New York (2013)
Gunn, J.F., Lester, D.: Twitter postings and suicide: an analysis of the postings of a fatal suicide in the 24 hours prior to death. Suicidologi 17(3), 28–30 (2012)
Homan, C., Johar, R., Liu, T., Lytle, M., Silenzio, V., Alm, C.O.: Toward macro-insights for suicide prevention: analyzing fine-grained distress at scale. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, ACL 2014, Baltimore, MD, USA, pp. 107–117 (2014)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, San Francisco, CA, USA, pp. 282–289 (2001)
Murphy, S.L., Kochanek, K.D., Xu, J., Arias, E.: Mortality in the united states. NCHS Data Brief. 229, 1–8 (2014)
Sueki, H.: The association of suicide-related twitter use with suicidal behaviour: a cross-sectional study of young internet users in Japan. J. Affect. Disord. 170, 155–160 (2015)
Sutton, C., McCallum, A.: An introduction to conditional random fields. Found. Trends Mach. Learn. 4(4), 267–373 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Moulahi, B., Azé, J., Bringay, S. (2017). DARE to Care: A Context-Aware Framework to Track Suicidal Ideation on Social Media. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-68786-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68785-8
Online ISBN: 978-3-319-68786-5
eBook Packages: Computer ScienceComputer Science (R0)