Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization

TL;DR

This study analyzes linguistic and emotional features of conspiracy theorists on X (Twitter), developing a machine learning classifier with high accuracy to identify such users based on their language patterns.

Contribution

The paper introduces a novel text-based approach to distinguish conspiracy theorists from regular users and characterizes their linguistic and emotional traits.

Findings

Marked linguistic differences between conspiracy theorists and others.

A machine learning classifier achieved an average F1 score of 0.88.

Identified key features that discriminate conspiracy propagators.

Abstract

In today's digital landscape, the proliferation of conspiracy theories within the disinformation ecosystem of online platforms represents a growing concern. This paper delves into the complexities of this phenomenon. We conducted a comprehensive analysis of two distinct X (formerly known as Twitter) datasets: one comprising users with conspiracy theorizing patterns and another made of users lacking such tendencies and thus serving as a control group. The distinguishing factors between these two groups are explored across three dimensions: emotions, idioms, and linguistic features. Our findings reveal marked differences in the lexicon and language adopted by conspiracy theorists with respect to other users. We developed a machine learning classifier capable of identifying users who propagate conspiracy theories based on a rich set of 871 features. The results demonstrate high accuracy, with an average F1 score of 0.88. Moreover, this paper unveils the most discriminating characteristics that define conspiracy theory propagators.