Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations

2025-08-05

AI Summary

Existing fake news detection methods predominantly rely on static modeling paradigms, failing to capture the dynamic evolution of misinformation veracity in response to shifting social contexts. To address this limitation, we propose MISDER, a Dynamic Environment-Aware Fake News Detection framework. MISDER reformulates detection as a temporal forecasting problem—predicting the evolution of social environment representations—by explicitly modeling dynamic contextual shifts. It integrates heterogeneous signals: news content, propagation topology, and multi-source social environment features. Three time-aware modeling variants are designed: LSTM-based sequence modeling, continuous-time modeling via ordinary differential equations (ODEs), and a pre-trained dynamical system. Extensive experiments on two benchmark datasets demonstrate that MISDER consistently outperforms state-of-the-art baselines, validating both the effectiveness and generalizability of dynamic environment representation for improving detection accuracy.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

Problem

• Detecting misinformation in dynamic social environments
• Overcoming limitations of static misinformation detection methods
• Learning temporal social environmental representations for accuracy

Innovation

• Dynamic environmental representations for misinformation detection
• LSTM, ODE, pre-trained dynamics as temporal models
• Social environmental representation learning per period