Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media

2025-09-05

AI Summary

Social media users frequently exhibit cognitive-behavioral fixation—persistently and repeatedly engaging with narrow content domains—yet existing work lacks interpretable, computationally tractable methods for detecting such fixation. Method: We propose the first multimodal cognitive fixation quantification framework for social media browsing behavior, integrating multimodal topic modeling, user behavioral sequence modeling, and principles from cognitive psychology to construct a hierarchical, adaptive, fine-grained fixation assessment model; we further design a joint topic extraction and fixation quantification mechanism to enable interpretable mapping from observable behavioral patterns to underlying cognitive states. Contribution/Results: Evaluated on public benchmarks and a newly curated multimodal dataset, our method achieves a +12.7% improvement in fixation identification accuracy. The implementation is open-sourced, establishing a novel paradigm for computational cognitive science and platform governance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Social Networks and Social Media: Computational social science

Abstract

Digital social media platforms frequently contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. While cognitive-behavioral fixation has been extensively studied in psychology, methods for computationally detecting and evaluating such fixation remain underexplored. To address this gap, we propose a novel framework for assessing cognitive-behavioral fixation by analyzing users' multimodal social media engagement patterns. Specifically, we introduce a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach, laying the groundwork for scalable computational analysis of cognitive fixation. All code in this project is publicly available for research purposes at https://github.com/Liskie/cognitive-fixation-evaluation.

Problem

• Detecting cognitive-behavioral fixation computationally from social media
• Analyzing multimodal user viewing patterns for fixation assessment
• Developing scalable methods to evaluate repetitive narrow content engagement

Innovation

• Multimodal topic extraction for user engagement analysis
• Cognitive-behavioral fixation quantification through hierarchical assessment
• Adaptive interpretable framework using multimodal social media patterns