Original title: Gender Attribution in Causal Beliefs
Zhuoyu Shi, Fred Morstatter · August 2026
People have long blamed women for harm while treating men as rational leaders. Researchers wanted to see if these biases still show up in how we explain causes and effects online today. They gathered all English posts on Twitter across three full 24-hour periods. Next, they used language models to pick out cause-and-effect pairs from the text and checked whether the cause was described as male or female. The team found clear patterns. People tied female causes to negative feelings and emotional or personal relationship outcomes. In contrast, they linked male causes to positive feelings and broad, structural effects. Finally, posts that treated men as the cause spread farther across different social groups, showing that old stereotypes still shape modern public debates.
You are in a study group where after every exam, people discuss why they did well or poorly. You start to notice a pattern: when a woman does poorly, others say she didn't study enough, but when a man does poorly, they say the test was hard. This unfair pattern is hard to spot because it happens in everyday conversations. In this paper, researchers used Twitter data to show that similar gender biases appear in how people explain causes online, with women blamed for negative outcomes and men credited for positive ones.
This matters because social media shapes how we perceive gender roles and blame. The study shows that even in casual tweets, women are more often blamed for negative, emotional outcomes, while men are credited with positive, structural ones. These patterns can reinforce stereotypes and influence real-world attitudes, from workplace evaluations to legal judgments.
The study only examines English Twitter posts from three days, so it may not represent other languages, platforms, or times. The language model used to extract causal statements could have biases or errors. The findings show correlations, not direct evidence of intentional bias or real-world impact.
Ask anything about this paper. Answers come from its abstract, and say so when the abstract doesn't cover your question.
Gender Attribution in Causal Beliefs — Zhuoyu Shi, Fred Morstatter
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.