# Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities

arXiv: https://arxiv.org/abs/2110.15269 · HTML: https://mlsanity.com/paper/2110.15269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [#lockdown: network-enhanced emotional profiling at the times of COVID-19](https://mlsanity.com/paper/2005.04404/) (2020)
- [Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media](https://mlsanity.com/paper/2003.08835/) (2020)
- [Revealing semantic and emotional structure of suicide notes with cognitive network science](https://mlsanity.com/paper/2007.12053/) (2020)
- [PyPlutchik: visualising and comparing emotion-annotated corpora](https://mlsanity.com/paper/2105.04295/) (2021)
- [Cognitive network science for understanding online social cognitions: A brief review](https://mlsanity.com/paper/2102.12799/) (2021)
- [Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests](https://mlsanity.com/paper/2008.12219/) (2020)

## Built on by (most cited first, 2 of 2)

- [From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora](https://mlsanity.com/paper/2403.14298/) (2024)
- [Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation](https://mlsanity.com/paper/2601.11758/) (2026)

## Abstract (arXiv)

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 conceptsin 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', 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 modelling 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 polarisation 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 r/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 quantitative comparisons provide strong evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valve. Our findings provide an interpretable, quantitative aid for supporting psychological inquiries of human feelings in digital and clinical settings.