Reduced speech coherence in psychosis-related social media forum posts.

IF 3 Q2 PSYCHIATRY
Laurin Plank, Armin Zlomuzica
{"title":"Reduced speech coherence in psychosis-related social media forum posts.","authors":"Laurin Plank, Armin Zlomuzica","doi":"10.1038/s41537-024-00481-1","DOIUrl":null,"url":null,"abstract":"<p><p>The extraction of linguistic markers from social media posts, which are indicative of the onset and course of mental disorders, offers great potential for mental healthcare. In the present study, we extracted over one million posts from the popular social media platform Reddit to analyze speech coherence, which reflects formal thought disorder and is a characteristic feature of schizophrenia and associated psychotic disorders. Natural language processing (NLP) models were used to perform an automated quantification of speech coherence. We could demonstrate that users who are active on forums geared towards disorders with a higher degree of psychotic symptoms tend to show a lower level of coherence. The lowest coherence scores were found in users of forums on dissociative identity disorder, schizophrenia, and bipolar disorder. In contrast, a relatively high level of coherence was detected in users of forums related to obsessive-compulsive disorder, anxiety, and depression. Users of forums on posttraumatic stress disorder, autism, and attention-deficit hyperactivity disorder exhibited medium-level coherence. Our findings provide promising first evidence for the possible utility of NLP-based coherence analyses for the early detection and prevention of psychosis on the basis of posts gathered from publicly available social media data. This opens new avenues for large-scale prevention programs aimed at high-risk populations.</p>","PeriodicalId":74758,"journal":{"name":"Schizophrenia (Heidelberg, Germany)","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224262/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Schizophrenia (Heidelberg, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s41537-024-00481-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0

Abstract

The extraction of linguistic markers from social media posts, which are indicative of the onset and course of mental disorders, offers great potential for mental healthcare. In the present study, we extracted over one million posts from the popular social media platform Reddit to analyze speech coherence, which reflects formal thought disorder and is a characteristic feature of schizophrenia and associated psychotic disorders. Natural language processing (NLP) models were used to perform an automated quantification of speech coherence. We could demonstrate that users who are active on forums geared towards disorders with a higher degree of psychotic symptoms tend to show a lower level of coherence. The lowest coherence scores were found in users of forums on dissociative identity disorder, schizophrenia, and bipolar disorder. In contrast, a relatively high level of coherence was detected in users of forums related to obsessive-compulsive disorder, anxiety, and depression. Users of forums on posttraumatic stress disorder, autism, and attention-deficit hyperactivity disorder exhibited medium-level coherence. Our findings provide promising first evidence for the possible utility of NLP-based coherence analyses for the early detection and prevention of psychosis on the basis of posts gathered from publicly available social media data. This opens new avenues for large-scale prevention programs aimed at high-risk populations.

Abstract Image

与精神病有关的社交媒体论坛帖子中的语音连贯性降低。
从社交媒体帖子中提取语言标记可反映精神障碍的发病和病程,这为精神保健提供了巨大的潜力。在本研究中,我们从流行的社交媒体平台 Reddit 上提取了 100 多万条帖子来分析语音连贯度,它反映了形式思维紊乱,是精神分裂症和相关精神障碍的特征之一。我们使用自然语言处理(NLP)模型对语音连贯性进行了自动量化。我们可以证明,活跃于精神症状程度较高的论坛的用户往往表现出较低的连贯性。在有关分离性身份识别障碍、精神分裂症和双相情感障碍的论坛中,用户的连贯性得分最低。相比之下,强迫症、焦虑症和抑郁症相关论坛用户的一致性水平相对较高。创伤后应激障碍、自闭症和注意力缺陷多动障碍论坛的用户则表现出中等程度的一致性。我们的研究结果首次证明,基于 NLP 的一致性分析可以在从公开社交媒体数据中收集的帖子的基础上,用于早期检测和预防精神病。这为针对高危人群的大规模预防计划开辟了新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信