DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media

2026-02-02

AI Summary

Existing approaches simplify social media misinformation engagement into homogeneous time series, overlooking the heterogeneous social mechanisms and platform-specific design differences underlying such interactions. This work addresses this limitation by drawing on social exchange theory to model the dynamic negotiation between user effort and social rewards as a context-adaptive sequence-to-sequence process. We propose a novel architecture that integrates decoupled representation learning, context-adaptive mechanisms, and cross-platform temporal modeling to effectively capture sentiment- and context-driven propagation dynamics. Evaluated on a large-scale dataset spanning seven platforms and 2.37 million posts, our model achieves a MAPE of 19.25%, outperforming the strongest baseline by 43.6% and revealing consistent behavioral patterns in cross-platform misinformation engagement.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Misinformation & Fake News

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Social media engagement prediction is a central challenge in computational social science, particularly for understanding how users interact with misinformation. Existing approaches often treat engagement as a homogeneous time-series signal, overlooking the heterogeneous social mechanisms and platform designs that shape how misinformation spreads. In this work, we ask: ``Can neural architectures discover social exchange principles from behavioral data alone?''We introduce \textsc{Dreams} (\underline{D}isentangled \underline{R}epresentations and \underline{E}pisodic \underline{A}daptive \underline{M}odeling for \underline{S}ocial media misinformation engagements), a social exchange theory-guided framework that models misinformation engagement as a dynamic process of social exchange. Rather than treating engagement as a static outcome, \textsc{Dreams} models it as a sequence-to-sequence adaptation problem, where each action reflects an evolving negotiation between user effort and social reward conditioned by platform context. It integrates adaptive mechanisms to learn how emotional and contextual signals propagate through time and across platforms. On a cross-platform dataset spanning $7$ platforms and 2.37M posts collected between 2021 and 2025, \textsc{Dreams} achieves state-of-the-art performance in predicting misinformation engagements, reaching a mean absolute percentage error of $19.25$\%. This is a $43.6$\% improvement over the strongest baseline. Beyond predictive gains, the model reveals consistent cross-platform patterns that align with social exchange principles, suggesting that integrating behavioral theory can enhance empirical modeling of online misinformation engagement. The source code is available at: https://github.com/ltian678/DREAMS.

Problem

• misinformation engagement
• social media
• social exchange theory
• computational social science
• user behavior

Innovation

• Social Exchange Theory
• Misinformation Engagement
• Disentangled Representation
• Cross-platform Modeling
• Sequence-to-Sequence Adaptation