Suicide Risk on Twitter

S. Fodeh, E. Boudreaux, Rixin Wang, Dennis Silva, R. Bossarte, J. Goulet, C. Brandt, Al-Talib Hamada
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引用次数: 3

Abstract

While many studies have explored the use of social media and behavioral changes of individuals, few examined the utility of using social media for suicide detection and prevention. The study by Jashinsky et al. identified specific language patterns associated with a set of twelve suicide risk factors. The authors extended these methods to assess the significance of the language used on Twitter for suicide detection. This article quantifies the use of Twitter to express suicide related language, and its potential to detect users at high risk of suicide. The authors searched Twitter for tweets indicative of 12 suicide risk factors. This paper divided Twitter users into two groups: “high risk” and “at risk” based on two of the risk factors (“self-harm” and “prior suicide attempts”) and examined language patterns by computing co-occurrences of terms in tweets which helped identify relationships between suicide risk factors in both groups.
推特上的自杀风险
虽然许多研究都探讨了社交媒体的使用和个人行为的改变,但很少有人研究了使用社交媒体进行自杀检测和预防的效用。Jashinsky等人的研究确定了与12种自杀风险因素相关的特定语言模式。作者扩展了这些方法,以评估Twitter上使用的语言对自杀检测的重要性。这篇文章量化了Twitter表达自杀相关语言的使用,以及它检测自杀高风险用户的潜力。作者在推特上搜索了12种自杀风险因素的推文。本文根据两个风险因素(“自残”和“有自杀企图”)将推特用户分为“高风险”和“有风险”两组,并通过计算推文中术语的共现率来检查语言模式,这有助于确定两组中自杀风险因素之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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