Classification of Helpful Comments on Online Suicide Watch Forums.
Classification of Helpful Comments on Online Suicide Watch Forums.
复制标题
在线自杀观察论坛上有用评论的分类。
DOI：
10.1145/2975167.2975170
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Cerel J
中科院分区：
文献类型：
--
作者：
Kavuluru R;Williams AG;Ramos-Morales M;Haye L;Holaday T;Cerel J
在社交媒体网站中，Reddit已经成为一个广泛使用的在线留言板，用于关注心理健康主题，包括抑郁症，成瘾和自杀观察（SW）。特别是，SW社区/子Reddit拥有近40，000名订阅者和13名人类版主，他们负责监控滥用评论等。鉴于对表达自杀想法的用户帖子的评论可以在任何时候从世界任何地方撰写，及时审核可能是乏味的。此外，Reddit的默认评论排名不涉及从自杀预防（SP）角度来看与评论的“有用性”相关的方面。能够从这样的角度自动识别和评分有帮助的评论可以帮助版主，帮助SW海报对评论的SP相关性进行即时反馈，并且还为SP研究人员提供了处理SP在线方面的见解。在本文中，我们报告了我们认为是自动识别SW论坛中在线帖子上的有用评论的第一次努力，SW subreddit作为用例。我们使用3000个真实的SW评论的数据集，并获得SP研究人员在相应原始帖子的上下文中对它们的有用性的判断。我们使用基于内容的特征（包括n-gram，单词心理测量分数和话语关系图）进行监督学习实验，并为有用的评论类报告了令人鼓舞的F分数（80 - 90%）。我们的研究结果表明，机器学习方法可以为SW帖子提供补充的调节功能。此外，我们意识到评估与心理健康相关的在线帖子的评论的有用性是一个微妙的话题，需要SP研究界进一步关注。
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.
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