What we write about when we write about causality: Features of causal statements across large-scale social discourse

TL;DR

This study analyzes causal statements on Twitter, revealing their linguistic features, emotional biases, and topical focus, thereby enhancing understanding of online causality communication.

Contribution

It provides a large-scale analysis of causal statements on social media, identifying linguistic and sentiment differences from non-causal statements.

Findings

Causal statements are more negative in sentiment than controls.

They exhibit distinct lexical and grammatical features.

Topics include news, health, and relationships.

Abstract

Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal statements made online. To study real-world causal attribution, we extract a large-scale corpus of causal statements made on the Twitter social network platform as well as a comparable random control corpus. We compare causal and control statements using statistical language and sentiment analysis tools. We find that causal statements have a number of significant lexical and grammatical differences compared with controls and tend to be more negative in sentiment than controls. Causal statements made online tend to focus on news and current events, medicine and health, or interpersonal relationships, as shown by topic models. By quantifying the features and potential biases of causality communication, this study improves our understanding of the accuracy of information and opinions found online.