The Anatomy of Conspirators: Unveiling Traits using a Comprehensive
Twitter Dataset
- URL: http://arxiv.org/abs/2308.15154v2
- Date: Mon, 5 Feb 2024 10:11:37 GMT
- Title: The Anatomy of Conspirators: Unveiling Traits using a Comprehensive
Twitter Dataset
- Authors: Margherita Gambini, Serena Tardelli, Maurizio Tesconi
- Abstract summary: We present a novel methodology for constructing a Twitter dataset that encompasses accounts engaged in conspiracy-related activities throughout the year 2022.
This comprehensive collection effort yielded a total of 15K accounts and 37M tweets extracted from their timelines.
We conduct a comparative analysis of the two groups across three dimensions: topics, profiles, and behavioral characteristics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The discourse around conspiracy theories is currently thriving amidst the
rampant misinformation in online environments. Research in this field has been
focused on detecting conspiracy theories on social media, often relying on
limited datasets. In this study, we present a novel methodology for
constructing a Twitter dataset that encompasses accounts engaged in
conspiracy-related activities throughout the year 2022. Our approach centers on
data collection that is independent of specific conspiracy theories and
information operations. Additionally, our dataset includes a control group
comprising randomly selected users who can be fairly compared to the
individuals involved in conspiracy activities. This comprehensive collection
effort yielded a total of 15K accounts and 37M tweets extracted from their
timelines. We conduct a comparative analysis of the two groups across three
dimensions: topics, profiles, and behavioral characteristics. The results
indicate that conspiracy and control users exhibit similarity in terms of their
profile metadata characteristics. However, they diverge significantly in terms
of behavior and activity, particularly regarding the discussed topics, the
terminology used, and their stance on trending subjects. In addition, we find
no significant disparity in the presence of bot users between the two groups.
Finally, we develop a classifier to identify conspiracy users using features
borrowed from bot, troll and linguistic literature. The results demonstrate a
high accuracy level (with an F1 score of 0.94), enabling us to uncover the most
discriminating features associated with conspiracy-related accounts.
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