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Measuring the co-evolution of online engagement with (mis)information and its visibility at scale

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Scalable temporal network modeling framework measuring co-evolution of online engagement with misinformation and its visibility using 100 million pandemic-related retweets. The framework can also be used to study other large-scale events where online attention is at stake, such as technological disruptions.

Summary

Online attention has become an increasingly valuable resource in the digital age, with extraordinary events like the COVID-19 pandemic fueling fierce competition for user engagement. As misinformation pervades online platforms, users seek credible sources while news outlets compete to attract and retain attention.

This research measures the co-evolution of online engagement with information and misinformation alongside its visibility, where engagement corresponds to user interactions on social media and visibility reflects fluctuations in user follower counts.

Using a scalable temporal network modeling framework applied to over 100 million COVID-related retweets spanning 3 years, the study reveals that highly engaged sources experience sharp spikes in follower growth during major events, while sources with questionable credibility tend to sustain faster growth outside of these periods.

Authors: Yueting Han (University of Warwick, The Alan Turing Institute), Paolo Turrini (University of Warwick), Marya Bazzi (sea.dev, University of Warwick), Giulia Andrighetto (National Research Council of Italy, Institute for Futures Studies Stockholm, Linkoping University), Eugenia Polizzi (National Research Council of Italy), Manlio De Domenico (University of Padua, Istituto Nazionale di Fisica Nucleare)

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