Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Misinformation Engagement on Social Media - Tian, Lin; Rizoiu, Marian-Andrei (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Understanding true influence in social media requires distinguishing correlation from causation—particularly when analyzing misinformation spread. While existing approaches focus on exposure metrics and network structures, they often fail to capture the causal mechanisms by which external temporal signals trigger engagement. We introduce CITRUS (Causal Influence through Treatment-Response Understanding in Social media), a novel joint treatment-outcome framework that leverages existing sequential models to understand how external signals—search trends, news coverage, influencer activity—trigger misinformation engagement. Through experiments on real-world misinformation and disinformation datasets, CITRUS outperforms existing benchmarks by 15-22% in predicting engagement across diverse counterfactual scenarios, including exposure adjustment, temporal alignment shifts, and varied intervention durations. Case studies on 492 social media users demonstrate that our causal effect measure aligns strongly with expert-based empirical influence assessments, validating CITRUS as a robust framework for understanding information spread dynamics. CITRUS also reveals that low-baseline misinformation can scale 6-fold under external promotion, showing super-linear growth, and unmasks hidden amplifiers—accounts with modest followings that double engagement rates, outperforming supposed "influencers" with 100x more followers.