ArticleEntropy (Basel, Switzerland)2025
Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political Communication Dynamics.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Mediating Similarity: An Information-Theoretic Principle of Reference Behavior.Entropy (Basel, Switzerland) · 2025Article
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2 authors.
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Abstract
Bots have become increasingly prevalent in the digital sphere and have taken up a proactive role in shaping democratic processes. While previous studies have focused on their influence at the individual level, their potential macro-level impact on communication dynamics remains underexplored. This study adopts an information-theoretic approach from dynamical systems theory to examine the role of political bots shaping the dynamics of an online political discussion on Twitter. We quantify the components of this dynamic process in terms of its complexity, predictability, and its entropy rate, or the remaining uncertainty. Findings suggest that bot activity is associated with increased complexity and, simultaneously, with more uncertainty in the structural dynamics of online political communication. While our dataset features earlier-generation bots, findings foreshadow the possibility for even more complex and uncertain online politics in the age of sophisticated and autonomous generative AI agents. Our presented framework showcases how this can be studied with the use of information-theoretic measures from dynamical systems theory.
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