The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text...

In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved.

This study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change).

We used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features.

The findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (β_intimacy=-1.706, P<.001), higher positive emotional polarity (β_{emotional_polarity}=.890, P<.001), lower specificity (β_specificity=-.018, P<.001), more first-person words (β_first-person=.120, P<.001), more future- and present-tense words (β_future-words=.301, P<.001), and fewer function words (β_{linguistic_style}=-.838, P<.001). The result is consistent with psychotherapists' psychotherapeutic strategy in offline counseling scenarios.

Our research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health.