Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04823 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911139007299584 |
|---|---|
| author | Wang, Yujie Zhao, Yunwei Yang, Jing Han, Han Shan, Shiguang Zhang, Jie |
| author_facet | Wang, Yujie Zhao, Yunwei Yang, Jing Han, Han Shan, Shiguang Zhang, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media Wang, Yujie Zhao, Yunwei Yang, Jing Han, Han Shan, Shiguang Zhang, Jie Social and Information Networks Computation and Language 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. |
| title | Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2509.04823 |