Abstract
Depression is a major threat to public health, accounting for almost 12% of all disabilities and claiming the life of 1 out of 5 patients suffering from it. Since depression is often signaled by decreasing social interaction, we explored how analysis of online health forums may help identify such episodes. We collected posts and replies from users of several forums on healthboards.com and analyzed changes in their use of language and activity levels over time. We found that users in the Depression forum use fewer social words, and have some revealing phrases associated with their last posts (e.g., cut myself). Our models based on these findings achieved 94 F1 for detecting users who will withdraw from a Depression forum by the end of a 1-year observation period.
Original language | English (US) |
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Title of host publication | EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the 4th International Workshop on Natural Language Processing for Social Media, SocialNLP 2016 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 14-19 |
Number of pages | 6 |
ISBN (Electronic) | 9781945626326 |
State | Published - 2016 |
Event | 4th ACM Workshop on Natural Language Processing for Social Media, SocialNLP 2016, associated with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States Duration: Nov 1 2016 → Nov 1 2016 |
Publication series
Name | EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the 4th International Workshop on Natural Language Processing for Social Media, SocialNLP 2016 |
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Conference
Conference | 4th ACM Workshop on Natural Language Processing for Social Media, SocialNLP 2016, associated with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 |
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Country/Territory | United States |
City | Austin |
Period | 11/1/16 → 11/1/16 |
Bibliographical note
Publisher Copyright:© 2016 Association for Computational Linguistics