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arXiv · 2405.12566

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

Alessandra Recordare·Guglielmo Cola·Tiziano Fagni·Maurizio Tesconi
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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.

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Alessandra Recordare, Guglielmo Cola, Tiziano Fagni, Maurizio Tesconi. 2024-05-21. Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization. https://arxiv.org/abs/2405.12566

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