Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.
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DOI：
10.1145/3110025.3123028
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Sheth A
中科院分区：
文献类型：
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作者：
Yazdavar AH;Al-Olimat HS;Ebrahimi M;Bajaj G;Banerjee T;Thirunarayan K;Pathak J;Sheth A
随着社交媒体的兴起，数百万人例行公事地在推特等社交媒体平台上表达自己的情绪、感受和日常与心理健康问题的斗争。与通过问卷调查和自我报告调查进行的传统观察性队列研究不同，我们探索从不引人注目的推文中可靠地检测出临床抑郁症。基于对Twitter个人资料中自报抑郁症状的用户爬行的推文的分析，我们展示了模仿临床医生使用的PHQ-9问卷检测临床抑郁症状的潜力。我们的研究使用半监督统计模型来评估这些症状的持续时间及其在Twitter上的表达(就词语使用模式和话题偏好而言)与通过PHQ-9报告的医学结果如何一致。我们的主动和自动筛查工具能够识别临床抑郁症状，准确率为68%，精确度为72%。
With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.