Studying expressions of loneliness in individuals using twitter: an observational study
Studying expressions of loneliness in individuals using twitter: an observational study
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DOI：
10.1136/bmjopen-2019-030355
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发表时间：
2019-11-01
期刊：
影响因子：
2.9
通讯作者：
Merchant, Raina
中科院分区：
文献类型：
--
作者：
Guntuku, Sharath Chandra;Schneider, Rachelle;Merchant, Raina
孤独是一个主要的公共卫生问题，据估计，美国18-70岁的成年人中有17%的人感到孤独。我们试图描述那些在推特上提到“孤独”或“孤独”的人的（在线）生活特征，并将他们的帖子与心理健康的预测因素联系起来。从2012年至2016年从美国宾夕法尼亚州的推特上收集的大约4亿条推文中，我们确定了推特上包含“孤独”或“孤独”字样的用户，并将他们与年龄、性别和发布时间相匹配的对照组进行了比较。使用自然语言处理，我们描述了用户帖子的主题和日常模式，它们与心理健康的语言标记的关联，以及语言是否可以预测孤独的表现。在2018-2019年进行了统计分析、数据综合和模型创建。我们评估了与对照组相比，帖子中包含孤独或孤独单词的用户的语言特征计数。这些语言特征通过(a)开放词汇主题，(b)语言探究词计数（LIWC）词汇，(c)愤怒、抑郁和焦虑的语言标记，以及(d)药物词汇的时间模式和数量来测量。利用机器学习，我们还评估了孤独感的表达是否可以通过曲线下面积（AUC）来预测用户的时间线。结果发现，6202名推特用户（n=6202）在推特上发布了包含孤独或孤独字样的帖子，其中包括人际关系困难、心身症状、药物使用、想要改变、不健康饮食和睡眠问题等主题。他们的帖子还与愤怒、抑郁和焦虑的语言标记有关。随机森林模型预测在线孤独感表达的AUC为0.86。用户使用孤独或孤独这两个词的Twitter时间轴通常包含社会心理特征，并可能与个人如何表达和体验孤独有关。这可以为经历孤独的高风险个体提供低资源在线评估，并为解决这种情况下的发病率提供干预措施。
Objectives Loneliness is a major public health problem and an estimated 17% of adults aged 18-70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words 'lonely' or 'alone' in their Twitter timeline and correlate their posts with predictors of mental health.Setting and design From approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words 'lonely' or 'alone' and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users' posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018-2019.Primary outcome measures We evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users' timelines, measured by area under curve (AUC).Results Twitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86.Conclusions Users' Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.