A Collective Intelligence Framework for Fake News Detection on Social Networks

Abstract

The rapid diffusion of user-generated content on social networks has amplified the reach of fake news, creating an urgent need for detection methods that combine scalability with epistemic robustness. This paper proposes a Collective Intelligence (CI) framework for fake news detection that formalizes the crowd assessing a news item as an intelligent collective characterized by diversity, independence, decentralization, and aggregation. A directed weighted graph is used to represent the collective, where vertices denote users, edge weights encode reputation-derived influence, and each user contributes a veracity judgment together with a set of stance-bearing content and context features. We instantiate collective independence through a reputation-based influence measure adapted from prior work and integrate the resulting independence-aware weights into a two-stage aggregation pipeline: (i) a supervised classifier that produces machine-generated veracity scores from news content, and (ii) a consensus operator that fuses machine scores with independence-weighted crowd signals. The framework is evaluated on the GossipCop split of the FakeNewsNet corpus, treating the tweet propagation graph associated with each news item as the collective. Experimental results show that the CI-based model outperforms content-only and unweighted crowd baselines in accuracy, precision, recall, and F1-score, and that independence-aware aggregation contributes the largest incremental gain among the four CI principles. These findings support the view that treating social-media crowds as structured collectives, rather than as bags of independent votes, yields measurable robustness against coordinated misinformation.