On a fine-grained French election retweet dataset, the multi-state voter model with zealots recovers individual users' party affiliations with 92.5% accuracy.
Echo chamber effects in signed networks
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Echo chamber effects in social networks are generally attributed to the prevalence of interactions among like-minded peers. However, recent evidence has emphasized the role of hostile interactions between opposite-minded groups. We investigate the role of polarization, identified with structural balance, in the formation of echo chambers in signed networks. To do so, we generalize the Independent Cascade Model and the Linear Threshold Model to describe information propagation in presence of negative edges. Antagonistic connections do not disrupt the flow of information, but instead, alter the way information is framed. Our results show that echo chambers spontaneously emerge in balanced networks, but also in antibalanced ones for specific parameters. This highlights that structural polarization and echo chambers do not necessarily display a one-to-one correspondence, showing instead a complex and often counterintuitive interplay. The robustness of our results is confirmed with a complex contagion model and through simulations in different network topologies, including real-world datasets.
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Voter model can accurately predict individual opinions in online populations
On a fine-grained French election retweet dataset, the multi-state voter model with zealots recovers individual users' party affiliations with 92.5% accuracy.