# Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement arXiv: https://arxiv.org/abs/2502.04655 ยท HTML: https://mlsanity.com/paper/2502.04655/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Core SSM architectures for language, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 31 of its 49 references that are in the map. - First posted on arXiv 7 Feb 2025. ## Placed in - State-space sequence models (S4, Mamba) / [Core SSM architectures for language](https://mlsanity.com/ssm/core-ssm-architectures-for-language/) ## Builds on (most cited first, 30 of 31) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022) - [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021) - [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020) - [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021) - [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024) - [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017) - [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022) - [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021) - [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019) - [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020) - [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018) - [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023) - [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016) - [Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity](https://mlsanity.com/paper/1602.06033/) (2016) - [Topological Recurrent Neural Network for Diffusion Prediction](https://mlsanity.com/paper/1711.10162/) (2017) - [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018) - [Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems](https://mlsanity.com/paper/2211.14114/) (2022) - [Efficient Non-parametric Bayesian Hawkes Processes](https://mlsanity.com/paper/1810.03730/) (2018) - [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023) - [Still out there: Modeling and Identifying Russian Troll Accounts on Twitter](https://mlsanity.com/paper/1901.11162/) (2019) - [Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions](https://mlsanity.com/paper/2109.00302/) (2021) - [Predicting the Role of Political Trolls in Social Media](https://mlsanity.com/paper/1910.02001/) (2019) - [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (2023) - [Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models](https://mlsanity.com/paper/1910.05451/) (2019) - [Interval-censored Hawkes processes](https://mlsanity.com/paper/2104.07932/) (2021) - [Rumour Detection via Zero-shot Cross-lingual Transfer Learning](https://mlsanity.com/paper/2109.12773/) (2021) - [Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos](https://mlsanity.com/paper/1801.04117/) (2018) - [Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions](https://mlsanity.com/paper/2208.06620/) (2022) ## Built on by (most cited first, 6 of 6) - [Mario at EXIST 2025: A Simple Gateway to Effective Multilingual Sexism Detection](https://mlsanity.com/paper/2507.10996/) (2025) - [DialogGuard: Multi-Agent Psychosocial Safety Evaluation of Sensitive LLM Responses](https://mlsanity.com/paper/2512.02282/) (2025) - [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025) - [Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement](https://mlsanity.com/paper/2505.19355/) (2025) - [DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media](https://mlsanity.com/paper/2602.01567/) (2026) - [Misinformation Without Triggers: From Factual Answers to Downstream Decisions](https://mlsanity.com/paper/2610.02886/) (2026) ## Abstract (arXiv) 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.