1 Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
2 Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia.
Crisis. 2017 Sep;38(5):319-329. doi: 10.1027/0227-5910/a000443. Epub 2017 Feb 23.
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.
This study aims to characterize the linguistic profiles of suicide-related Twitter posts.
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.
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.
The predictive validity of the identified features needs further testing before these results can be used for interventional purposes.
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.
自杀是全球范围内主要的死亡原因之一。识别高危人群并及时进行干预具有挑战性。社交媒体网站 Twitter 被用于表达自杀倾向。对与自杀相关的帖子进行自动化语言分析，可能有助于区分需要支持或干预的人与不需要的人。
本研究旨在描述与自杀相关的 Twitter 帖子的语言特征。
使用先前由专家对自杀风险进行编码的与自杀相关的 Twitter 帖子数据集，进行了 Linguistic Inquiry and Word Count（LIWC）和回归分析，以确定语言特征的差异。
与匹配的非自杀相关的 Twitter 帖子相比，高度关注自杀相关的帖子的特点是字数更多，第一人称代词的使用增加，以及更多与死亡相关的内容。与可忽略不计的自杀相关的帖子相比，高度关注自杀相关的帖子的特点是第一人称代词的使用增加，愤怒情绪增加，以及对当下的关注增加。还发现了其他差异。
在这些结果可用于干预目的之前，需要进一步测试所确定特征的预测有效性。
本研究表明，高度关注自杀相关的 Twitter 帖子具有独特的语言特征。检查 Twitter 数据中是否存在这些特征，可能有助于验证在线风险评估，并确定需要进一步支持或干预的人群。
