论文标题
预测精神障碍在线社区中的用户情感语气
Predicting User Emotional Tone in Mental Disorder Online Communities
论文作者
论文摘要
近年来,在线社交网络已成为患有精神障碍的人分享艰辛并获得情感和信息支持的人们的重要媒介。在这项工作中,我们分析了与精神障碍有关的Reddit社区的讨论如何帮助改善用户的健康状况。使用用户写作的情感语气作为情绪状态的代理,我们发现用户互动与状态变化之间的关系。首先,我们观察到,负面帖子的作者经常在讨论讨论后写下鲜艳的评论,这表明用户的情绪状态可以由于社会支持而改善。其次,我们基于SOTA文本嵌入技术和RNN构建模型,以预测情绪语调的转变。这与大多数相关工作不同,后者主要侧重于检测用户活动的精神障碍。我们证明了准确预测用户对这些平台中经历的交互的反应的可行性,并提供了一些示例,这些示例说明了模型正确地捕获了评论对作者情感语调的影响。我们的模型对干预措施具有令人鼓舞的意义,以为挣扎精神疾病的人们提供支持。
In recent years, Online Social Networks have become an important medium for people who suffer from mental disorders to share moments of hardship, and receive emotional and informational support. In this work, we analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users. Using the emotional tone of users' writing as a proxy for emotional state, we uncover relationships between user interactions and state changes. First, we observe that authors of negative posts often write rosier comments after engaging in discussions, indicating that users' emotional state can improve due to social support. Second, we build models based on SOTA text embedding techniques and RNNs to predict shifts in emotional tone. This differs from most of related work, which focuses primarily on detecting mental disorders from user activity. We demonstrate the feasibility of accurately predicting the users' reactions to the interactions experienced in these platforms, and present some examples which illustrate that the models are correctly capturing the effects of comments on the author's emotional tone. Our models hold promising implications for interventions to provide support for people struggling with mental illnesses.