背景:用户越来越多地使用社交网络服务(SNS)来分享他们的感受和情感。对于那些有精神障碍的人,SNS也可用于寻求有关心理健康问题的建议。一个可用的SNS是Reddit,用户可以在相关的健康诊断子目录上自由讨论此类问题。
目的:在本研究中,我们分析了用户帖子中关于特定精神障碍的独特语言特点(抑郁症,焦虑,双相情感障碍,边缘性人格障碍,精神分裂症,自闭症,和心理健康),并通过将它们与与精神疾病无关的subreddits帖子进行比较,进一步验证了它们的外部独特性。我们还证实,语言表述中的这些差异可以通过机器学习过程来学习。
方法:用户上传的Reddit帖子是为我们的研究而收集的。我们在语言查询和字数(LIWC)软件中使用了各种统计分析方法,包括单向方差分析和随后的事后检验,查看与心理健康相关的子词中各种词汇特征的情感差异,以及与不相关的差异。我们还在这两种情况下应用了3种监督和无监督聚类方法,之后使用来自转换器(BERT)的双向编码器表示从每个subreddit上的帖子中提取文本特征,以确保我们的数据集适用于进一步的机器学习或深度学习任务。
结果:我们收集了919,722个Reddit用户的3,133,509个帖子。使用数据的结果表明,subreddits之间存在显著的语言差异,与先前研究的结果一致。LIWC分析的结果表明,每个心理健康问题的患者都表现出明显不同的词汇和语义模式,例如字数或情感,在他们的在线社交网络活动中,所有病例的P<.001。此外,通过监督和无监督聚类方法成功识别了每个subreddit组的独特特征,使用从文本帖子中提取的BERT嵌入。在前一种情况下,戴维斯-博尔丁的分数从0.222到0.397,轮廓分数从0.639到0.803,反映了这种独特性,得分分别为1.638和0.729,在后一种情况下。
结论:通过采取多方面的方法,使用统计学分析与心理健康问题相关的文本帖子,自然语言处理,和机器学习技术,我们的方法提供了对最近的词汇用法和有关特定心理健康问题患者的语言特点的信息方面的见解,这可以告知临床医生关于患者的心理健康的诊断条款,以帮助在线干预。我们的发现可以通过在线识别和检测精神弱势群体,进一步促进涉及语言分析和机器学习方法的研究领域。
Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits.
In this study, we analyzed the distinctive linguistic characteristics in users\' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process.
Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks.
We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case.
By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients\' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.