Proactive Misinformation Forecasting via Incremental Topic Detection and Sentiment Polarization

The unprecedented scale and velocity of unverified content on online social media platforms (OSMPs) threatens public discourse, emergency response, and democratic processes. This paper operationalizes a socio-cognitive hypothesis, that polarization and confirmation bias create "fertile ground"for misinformation, into an engineering framework for proactive topic risk forecasting. We introduce a semi-supervised pipeline that continuously tracks topics in a tweet stream and measures topic-specific sentiment divergence as an early warning signal. Our core methodology integrates: (i) Incremental Clustering with Burst Detection (ICBD), an online clustering algorithm that adapts to topic drift via exponentially weighted centroid updates and identifies volatility through a statistical burst detector; and (ii) a Topic-specific Sentiment Classifier (TSC) that conditions sentiment inference on topic context, reducing ambiguity in polarizing language. The resulting polarization metrics is used as a predictive feature in ensemble veracity models. On two large-scale COVID-19 Twitter collections, adding polarization-aware features yields consistent gains across baselines and reaches an F1-score of 0.879 with XGBoost. Beyond accuracy, we demonstrate a one-month lead-time forecasting protocol where polarization features improve F1 by 11.9%, validating their suitability for preemptive moderation and public-information interventions.