# Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers

arXiv: https://arxiv.org/abs/2310.17369 · HTML: https://mlsanity.com/paper/2310.17369/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales](https://mlsanity.com/paper/1309.5909/) (2013)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering](https://mlsanity.com/paper/1408.1293/) (2014)
- [Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Age](https://mlsanity.com/paper/2306.05387/) (2023)

## Built on by (most cited first, 11 of 11)

- [Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health](https://mlsanity.com/paper/2403.02281/) (2024)
- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs](https://mlsanity.com/paper/2410.20334/) (2024)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions](https://mlsanity.com/paper/2405.18061/) (2024)
- [CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models](https://mlsanity.com/paper/2503.10707/) (2025)
- [JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai Community](https://mlsanity.com/paper/2503.21679/) (2025)
- [Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations](https://mlsanity.com/paper/2507.21089/) (2025)
- [Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement](https://mlsanity.com/paper/2601.13481/) (2026)
- [NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey](https://mlsanity.com/paper/2602.15866/) (2026)
- [Politics of Feelings: Emotional Expression and Legislative Effectiveness in the U.S. Congress](https://mlsanity.com/paper/2609.10198/) (2026)

## Abstract (arXiv)

Research in psychopathology has shown that, at an aggregate level, the patterns of emotional change over time -- emotion dynamics -- are indicators of one's mental health. One's patterns of emotion change have traditionally been determined through self-reports of emotions; however, there are known issues with accuracy, bias, and ease of data collection. Recent approaches to determining emotion dynamics from one's everyday utterances addresses many of these concerns, but it is not yet known whether these measures of utterance emotion dynamics (UED) correlate with mental health diagnoses. Here, for the first time, we study the relationship between tweet emotion dynamics and mental health disorders. We find that each of the UED metrics studied varied by the user's self-disclosed diagnosis. For example: average valence was significantly higher (i.e., more positive text) in the control group compared to users with ADHD, MDD, and PTSD. Valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. Rise and recovery rates of valence also exhibited significant differences from the control. This work provides important early evidence for how linguistic cues pertaining to emotion dynamics can play a crucial role as biosocial markers for mental illnesses and aid in the understanding, diagnosis, and management of mental health disorders.