Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization
<|reference_start|>Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization: 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.<|reference_end|>
@article{recordare2024unveiling,
    title={Unveiling Online Conspiracy Theorists: a Text-Based Approach and
  Characterization},
    author={Alessandra Recordare, Guglielmo Cola, Tiziano Fagni and Maurizio
  Tesconi},
    journal={arXiv preprint arXiv:2405.12566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12566},
    primaryClass={cs.SI cs.CY}
}