A Linguistic Analysis of Suicide-Related Twitter Posts
A Linguistic Analysis of Suicide-Related Twitter Posts
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DOI：
10.1027/0227-5910/a000443
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发表时间：
2017-09-01
影响因子：
3
通讯作者：
Christensen, Helen
中科院分区：
文献类型：
--
作者：
O'Dea, Bridianne;Larsen, Mark E.;Christensen, Helen
背景：自杀是世界范围内的主要死亡原因。确定那些有风险的人并及时采取干预措施是一项挑战。社交媒体网站Twitter被用来表达自杀倾向。对自杀相关帖子的自动语言分析可能有助于区分那些需要支持或干预的人。目的：本研究旨在描述自杀相关Twitter帖子的语言特征。方法：使用先前由专家为自杀风险编码的自杀相关Twitter帖子的数据集，进行语言查询和字数统计（LIWC）和回归分析，以确定语言特征的差异。结果如下：与匹配的非自杀相关的Twitter帖子相比，强烈关注自杀相关的帖子的特点是字数更高，第一人称代词的使用增加，以及更多地提到死亡。与可以忽略的自杀相关帖子相比，强烈关注自杀相关帖子的特点是更多地使用第一人称代词，更大的愤怒和更多地关注现在。其他差异被发现。局限性：所确定的功能的预测有效性需要进一步测试，这些结果可以用于干预的目的。结论：这项研究表明，强烈关注自杀相关的Twitter帖子有独特的语言特征。检查推特数据中是否存在此类功能，可能有助于验证在线风险评估，并确定哪些人需要进一步支持或干预。
Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. Aims: This study aims to characterize the linguistic profiles of suicide-related Twitter posts. Method: Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles. Results: When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found. Limitations: The predictive validity of the identified features needs further testing before these results can be used for interventional purposes. Conclusion: This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.