# Measuring Emotional Contagion in Social Media

arXiv: https://arxiv.org/abs/1506.06021 · HTML: https://mlsanity.com/paper/1506.06021/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2015.

## Built on by (most cited first, 10 of 10)

- [Consistency of Responses and Continuations Generated by Large Language Models on Social Media](https://mlsanity.com/paper/2501.08102/) (2025)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [LEIA: Linguistic Embeddings for the Identification of Affect](https://mlsanity.com/paper/2304.10973/) (2023)
- [Wrong Face, Wrong Move: The Social Dynamics of Emotion Misperception in Agent-Based Models](https://mlsanity.com/paper/2509.00080/) (2025)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-Poor Language](https://mlsanity.com/paper/2407.12141/) (2024)
- [Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://mlsanity.com/paper/2601.01090/) (2026)
- [#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic](https://mlsanity.com/paper/2406.01866/) (2024)
- [Negative news posts are less prevalent and generate lower user engagement than non-negative news posts across six countries](https://mlsanity.com/paper/2507.19300/) (2025)
- [The geometry of online conversations and the causal antecedents of conflictual discourse](https://mlsanity.com/paper/2602.15600/) (2026)

## Abstract (arXiv)

Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.