AI for Social Good — Part 1
With the advent of social media, the text data is available in abundance. People share opinions not just about products and services but some also share their feelings. Natural Language Processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions.
Suicidal Ideation Detection
Suicide is a crucial issue in current society. Early discovery and prevention of suicide attempt should be addressed to save people’s life. Current suicidal ideation detection methods include clinical methods based on the communication between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic discovery based on online social contents [1]. Due to the advances of social media and online anonymity, a growing number of individuals turn to communicate with others on the Internet. Online communication channels are becoming a new way [2,3] for people to express their feelings, suffering, and suicidal tendencies.
Users’ post on social websites reveals rich information and their language preferences. Suicide-related keyword dictionary and lexicon are manually built to enable keyword filtering [4, 5] and phrases filtering [6]. Suicide-related keywords and phrases include “kill”, “suicide”, “feel alone”, “depressed”, and “cutting myself”.
There is a concerning trend that potential suicide victims post their suicidal thoughts on social media platforms like Facebook, Twitter, and Reddit. Social media platforms are becoming an assuring tunnel for observing suicidal thoughts and stopping suicide attempts [7]. Massive user-generated data provide an excellent source to study online users’ language patterns. Using NLP on social networks and applying machine learning techniques provide an avenue to understand the intent within online posts, provide early warnings, and even relieve a person's suicidal intentions. Twitter offers a good source for research on suicidality. O’Dea et al. [8] collected tweets using the public API. They developed automatic suicide detection by applying logistic regression and SVM on TF-IDF features. Wang et al. [9] further improved the performance with practical feature engineering. Shepherd et al. [10] conducted psychology-based data analysis for contents that suggests suicidal tendencies in Twitter social networks. The authors used the data from an online conversation called #dearmentalhealthprofessionals.
Another popular platform Reddit is an online forum with topic-specific discussions has also attracted much research interest in investigating mental health issues [11] and suicidal ideation. A community on Reddit called SuicideWatch is intensively used for examining suicidal intention. De Choudhury et al. [12] applied a statistical methodology to discover the transition from mental health issues to suicidality. Kumar et al. [13] examined the posting activity following the celebrity suicides, studied the effect of celebrity suicides on suicide-related contents, and proposed a method to prevent the high-profile suicides. Many researchers [14], [15] work on detecting suicidal ideation in Chinese microblogs. Guan et al. [16] studied user profile and linguistic features for estimating suicide probability in Chinese microblogs. There also remains some work using other platforms for suicidal ideation detection. For example, Cash et al. [17] conducted a study on adolescents’ comments and content analysis on MySpace. Steaming data provides a good source for user pattern analysis. Vioules et al. [ 18] conducted user-centric and post-centric behaviour analysis. They applied a martingale framework to detect sudden emotional changes in the Twitter data stream for monitoring suicide warning signs. Blog stream collected from public blog articles written by suicide victims is used by Ren et al. [19] to study the accumulated emotional information.
Workshops
- https://aiforsocialgood.github.io/neurips2019/
- https://aiforsocialgood.github.io/iclr2019/
- https://spssi.onlinelibrary.wiley.com/journal/15404560
[1] Ji, S., Pan, S., Li, X., Cambria, E., Long, G. and Huang, Z., 2019. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. arXiv preprint arXiv:1910.12611.
[2] Pennell, D.L. and Liu, Y., 2014. Normalization of informal text. Computer Speech & Language, 28(1), pp.256–277.
[3] Satapathy, R., Guerreiro, C., Chaturvedi, I. and Cambria, E., 2017, November. Phonetic-based microtext normalization for twitter sentiment analysis. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 407–413). IEEE.
[4] Y.-P. Huang, T. Goh, and C. L. Liew, “Hunting suicide notes in web 2.0-preliminary findings,” in Multimedia Workshops, 2007. ISMW’07. Ninth IEEE International Symposium on. IEEE, 2007, pp. 517–521.
[5] K. D. Varathan and N. Talib, “Suicide detection system based on twitter,” in Science and Information Conference (SAI), 2014. IEEE, 2014, pp. 785–788.
[6] J. Jashinsky, S. H. Burton, C. L. Hanson, J. West, C. Giraud-Carrier, M. D. Barnes, and T. Argyle, “Tracking suicide risk factors through twitter in the us,” Crisis, 2014.
[7] J. Robinson, G. Cox, E. Bailey, S. Hetrick, M. Rodrigues, S. Fisher, and H. Herrman, “Social media and suicide prevention: a systematic review,” Early intervention in psychiatry, vol. 10, no. 2, pp. 103–121, 2016.
[8] B. O’Dea, S. Wan, P. J. Batterham, A. L. Calear, C. Paris, and H. Christensen, “Detecting suicidality on twitter,” Internet Interventions, vol. 2, no. 2, pp. 183–188, 2015.
[9] Y. Wang, S. Wan, and C. Paris, “The role of features and context on suicide ideation detection,” in Proceedings of the Australasian Language Technology Association Workshop 2016, 2016, pp. 94–102.
[10] A. Shepherd, C. Sanders, M. Doyle, and J. Shaw, “Using social media for support and feedback by mental health service users: thematic analysis of a twitter conversation,” BMC psychiatry, vol. 15, no. 1, p. 29, 2015.
[11] M. De Choudhury and S. De, “Mental health discourse on reddit: Self-disclosure, social support, and anonymity.” in ICWSM, 2014.
[12] M. De Choudhury, E. Kiciman, M. Dredze, G. Coppersmith, and M. Kumar, “Discovering shifts to suicidal ideation from mental health content in social media,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016, pp. 2098–2110.
[13] M. Kumar, M. Dredze, G. Coppersmith, and M. De Choudhury, “Detecting changes in suicide content manifested in social media following celebrity suicides,” in Proceedings of the 26th ACM Conference on Hypertext & Social Media. ACM, 2015, pp. 85–94.
[14] X. Huang, L. Zhang, D. Chiu, T. Liu, X. Li, and T. Zhu, “Detecting suicidal ideation in chinese microblogs with psychological lexicons,” in Ubiquitous Intelligence and Computing, 2014 IEEE 11th Intl Conf on and IEEE 11th Intl Conf on and Autonomic and Trusted Computing, and IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom). IEEE, 2014, pp. 844–849.
[15] X. Huang, X. Li, T. Liu, D. Chiu, T. Zhu, and L. Zhang, “Topic model for identifying suicidal ideation in chinese microblog,” in Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, 2015, pp. 553–562
[16] L. Guan, B. Hao, Q. Cheng, P. S. Yip, and T. Zhu, “Identifying chinese microblog users with high suicide probability using internet-based profile and linguistic features: classification model,” JMIR mental health, vol. 2, no. 2, p. e17, 2015.
[17] S. J. Cash, M. Thelwall, S. N. Peck, J. Z. Ferrell, and J. A. Bridge, “Adolescent suicide statements on myspace,” Cyberpsychology, Behavior, and Social Networking, vol. 16, no. 3, pp. 166–174, 2013.
[18] M. J. Vioules, B. Moulahi, J. Az ` e, and S. Bringay, “Detection ´ of suicide-related posts in twitter data streams,” IBM Journal of Research and Development, vol. 62, no. 1, pp. 7:1–7:12, 2018.
[19] ] F. Ren, X. Kang, and C. Quan, “Examining accumulated emotional traits in suicide blogs with an emotion topic model,” IEEE journal of biomedical and health informatics, vol. 20, no. 5, pp. 1384–1396, 2016.
