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Citation |
Kavuluru R, Williams AG, Ramos-Morales M, Haye L, Holaday T, Cerel J. Proc. ACM BCB 2016; 2016: 32-40. |
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Affiliation |
College of Social Work University of Kentucky Lexington, Kentucky. |
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Copyright |
(Copyright © 2016, Association for Computing Machinery) |
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DOI |
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PMID |
28736770 |
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PMCID |
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Abstract |
Among social media websites, Reddit has emerged as a widely used online message board for focused mental health topics including depression, addiction, and suicide watch (SW). In particular, the SW community/subreddit has nearly 40,000 subscribers and 13 human moderators who monitor for abusive comments among other things. Given comments on posts from users expressing suicidal thoughts can be written from any part of the world at any time, moderating in a timely manner can be tedious. Furthermore, Reddit's default comment ranking does not involve aspects that relate to the "helpfulness" of a comment from a suicide prevention (SP) perspective. Being able to automatically identify and score helpful comments from such a perspective can assist moderators, help SW posters to have immediate feedback on the SP relevance of a comment, and also provide insights to SP researchers for dealing with online aspects of SP. In this paper, we report what we believe is the first effort in automatic identification of helpful comments on online posts in SW forums with the SW subreddit as the use-case. We use a dataset of 3000 real SW comments and obtain SP researcher judgments regarding their helpfulness in the contexts of the corresponding original posts. We conduct supervised learning experiments with content based features including n-grams, word psychometric scores, and discourse relation graphs and report encouraging F-scores (≈ 80 - 90%) for the helpful comment classes. Our results indicate that machine learning approaches can offer complementary moderating functionality for SW posts. Furthermore, we realize assessing the helpfulness of comments on mental health related online posts is a nuanced topic and needs further attention from the SP research community. Language: en |
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Keywords |
suicide prevention; text classification |