Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities

Simmi Marina Joseph, Salvatore Citraro, Virginia Morini, Giulio Rossetti and Massimo Stella

Physica A: Statistical Mechanics and its Applications, 2023, vol. 610, issue C

Abstract: Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding “feel”, stem for “to feel” and “feelings”, enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of “regret”, “unhealthy lifestyle” and “low mental well-being”. Importantly, negative associations and emotions co-existed with trustful/positive language, focused on “getting better”. This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.

Keywords: Complex networks; Topic modeling; Emotions; Corpus; Concepts (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:610:y:2023:i:c:s0378437122008949

DOI: 10.1016/j.physa.2022.128336

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