Are Generics and Negativity about Social Groups Common on Social Media? A Comparative Analysis of Twitter (X) Data

TL;DR

This study uses machine learning to analyze over a million tweets, revealing that most tweets lack social generics, but those containing generics tend to be more liked and retweeted, with notable differences across social group types.

Contribution

First ML-based analysis of social generics on Twitter, showing their prevalence, impact, and variation across social groups and political contexts.

Findings

78% of tweets contain no social generics

Tweets with social generics receive more likes and retweets

Negative generics about political groups are more common and retweeted than those about ethnic groups

Abstract

Generics (unquantified generalizations) are thought to be pervasive in communication and when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to more than a million tweets about people, and analyzed the tweets. We found that most tweets (78%) about people contained no generics. However, tweets with social generics received more 'likes' and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter.