Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910454213771264 |
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| author | Recordare, Alessandra Cola, Guglielmo Fagni, Tiziano Tesconi, Maurizio |
| author_facet | Recordare, Alessandra Cola, Guglielmo Fagni, Tiziano Tesconi, Maurizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization Recordare, Alessandra Cola, Guglielmo Fagni, Tiziano Tesconi, Maurizio Social and Information Networks Computers and Society 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. |
| title | Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2405.12566 |