Control of Misinformation with Safety and Engagement Guarantees

Misinformation (e.g., rumors and fake news) on social networks poses substantial threats in various domains, underscoring the urgent need for interventions to address misinformation propagation. We present an edge-based optimal control algorithm to minimize the spread of misinformation over social networks. The proposed algorithm preserves the level of information each network node receives while preventing rumors from going viral. We introduce a localized version of the algorithm to enable scaling to large networks. Localization is based on removing any connecting edges between two nodes in the underlying graph that exceed the predefined localization distance in the latent space, whereby the probability of interaction between the two nodes decays exponentially with the distance between them in the latent space. The proposed localized control algorithm compensates for the truncation errors and retains the baseline algorithm's guarantees while reducing computational complexity. Empirical studies applying the baseline algorithm on random spatial networks and its localized counterpart on large-scale social networks extracted from 100 million Twitter messages reduced the ratio of infected users by 8 % in synthetic networks and up to 6 % on real-world social networks while preserving the level of information each node receives and preventing viral rumors.