Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement

TL;DR

This paper introduces IC-Mamba, a state space model that accurately predicts early social media engagement and narrative patterns of misinformation, enabling timely interventions to counteract harmful content.

Contribution

We develop IC-Mamba, a novel state space model that effectively models irregularly sampled engagement data and improves early prediction accuracy for misinformation spread.

Findings

IC-Mamba achieves 4.72% better accuracy than existing models.

It predicts engagement within 15-30 minutes with low RMSE (0.118-0.143).

The model forecasts engagement up to 28 days ahead with high accuracy.

Abstract

In today's digital age, conspiracies and information campaigns can emerge rapidly and erode social and democratic cohesion. While recent deep learning approaches have made progress in modeling engagement through language and propagation models, they struggle with irregularly sampled data and early trajectory assessment. We present IC-Mamba, a novel state space model that forecasts social media engagement by modeling interval-censored data with integrated temporal embeddings. Our model excels at predicting engagement patterns within the crucial first 15-30 minutes of posting (RMSE 0.118-0.143), enabling rapid assessment of content reach. By incorporating interval-censored modeling into the state space framework, IC-Mamba captures fine-grained temporal dynamics of engagement growth, achieving a 4.72% improvement over state-of-the-art across multiple engagement metrics (likes, shares, comments, and emojis). Our experiments demonstrate IC-Mamba's effectiveness in forecasting both post-level dynamics and broader narrative patterns (F1 0.508-0.751 for narrative-level predictions). The model maintains strong predictive performance across extended time horizons, successfully forecasting opinion-level engagement up to 28 days ahead using observation windows of 3-10 days. These capabilities enable earlier identification of potentially problematic content, providing crucial lead time for designing and implementing countermeasures. Code is available at: https://github.com/ltian678/ic-mamba. An interactive dashboard demonstrating our results is available at: https://ic-mamba.behavioral-ds.science.