# From Individual Expression to Group Polarization: A Study on Twitter’s Emotional Diffusion Patterns in the German Election

## TL;DR

This study examines how emotions, especially negativity, spread on Twitter during the 2021 German election, showing that negative sentiments and derogatory speech spread more widely than positive ones.

## Contribution

The study introduces a detailed analysis of emotional diffusion and derogatory speech patterns in political social media discourse during an election.

## Key findings

- Negative emotions dominated social media discussions during the German election.
- Derogatory speech targeted candidates in four forms, with female candidates disproportionately affected.
- Negative emotions spread significantly more than positive or neutral sentiments on social media.

## Abstract

This study analyzes 194,151 tweets from the 2021 German federal election using sentiment analysis and statistical techniques to examine social media’s role in shaping group emotions, voters’ emotional expression and derogatory speech toward candidates, and the relationship between sentiment intensity and tweet spread. The findings show that negative emotions dominated social media discussions. Additionally, voter perceptions towards candidates on social media also follow a pattern of negativity, often characterized by derogatory speech. This takes four main forms: intelligence-based attacks, animal metaphors, character insults, and gender-based discrimination, with female candidates disproportionately affected. Moreover, the study finds that negative emotions exhibit significantly greater diffusion and reach compared to positive and neutral sentiments on social media. This study further examines election fairness and political dialog openness through the lens of equity, inclusion, diversity, and access (IDEA). These findings emphasize individual and collective emotional dynamics in the social media environment, highlighting the need for governance strategies that promote equity, inclusivity, and diversity in digital political discussions.