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

arXiv: https://arxiv.org/abs/2509.04823 · HTML: https://mlsanity.com/paper/2509.04823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)
- [Debunking in a World of Tribes](https://mlsanity.com/paper/1510.04267/) (2015)
- [Multilingual and Multimodal Topic Modelling with Pretrained Embeddings](https://mlsanity.com/paper/2211.08057/) (2022)
- [Neural Multimodal Topic Modeling: A Comprehensive Evaluation](https://mlsanity.com/paper/2403.17308/) (2024)
- [Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling](https://mlsanity.com/paper/2409.15626/) (2024)
- [Bursty Human Dynamics](https://mlsanity.com/paper/1803.02580/) (2018)
- [Topicality and Social Impact: Diverse Messages but Focused Messengers](https://mlsanity.com/paper/1402.5443/) (2014)
- [Entropy-based detection of Twitter echo chambers](https://mlsanity.com/paper/2308.01750/) (2023)

## Built on by (most cited first, 1 of 1)

- [PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media](https://mlsanity.com/paper/2605.12525/) (2026)

## Abstract (arXiv)

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.