通过多模态行为分析，量化用户在社交媒体上的认知固化程度。
Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media
- 融合多模态数据提取话题，动态评估用户行为模式
- 在新构建的数据集上验证了方法的有效性
- 适合关注信息茧房与心理行为分析的研究者
数字社交媒体平台常导致认知行为固化现象，即用户持续且重复地聚焦于狭窄内容领域。尽管该现象在心理学中已有广泛研究，但其计算检测与评估方法仍不充分。为此，本文提出一种新框架，通过分析用户的多模态社交媒体参与模式来评估认知行为固化。具体而言，引入一个多模态话题提取模块和一个认知行为固化量化模块，协同实现自适应、分层且可解释的用户行为评估。在现有基准数据集和新构建的多模态数据集上的实验表明，该方法有效，为大规模认知固化的计算分析奠定了基础。本项目所有代码已公开，供研究使用：https://github.com/Liskie/cognitive-fixation-evaluation。
原文摘要 · Abstract (English)
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.
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