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

• URL: http://arxiv.org/abs/2509.04823v1
• Date: Fri, 05 Sep 2025 05:50:00 GMT
• Title: Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media
• Authors: Yujie Wang, Yunwei Zhao, Jing Yang, Han Han, Shiguang Shan, Jie Zhang,
• Abstract summary: We propose a novel framework for assessing cognitive-behavioral fixation by analyzing users' multimodal social media engagement patterns.<n> Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach.
• Score: 52.313084466769375
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Stochastic Encodings for Active Feature Acquisition [100.47043816019888]
  Active Feature Acquisition is an instance-wise, sequential decision making problem.<n>The aim is to dynamically select which feature to measure based on current observations, independently for each test instance.<n>Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic.<n>We introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a latent space.
  arXiv  Detail & Related papers  (2025-08-03T23:48:46Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• A Comprehensive Review on Hashtag Recommendation: From Traditional to Deep Learning and Beyond [0.37865171120254354]
  Hashtags, as a fundamental categorization mechanism, play a pivotal role in enhancing content visibility and user engagement.<n>The development of accurate and robust hashtag recommendation systems remains a complex and evolving research challenge.<n>This review article conducts a systematic analysis of hashtag recommendation systems, examining recent advancements across several dimensions.
  arXiv  Detail & Related papers  (2025-03-24T13:40:36Z)
• How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review [16.35521789216079]
  Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents.<n>Existing overviews remain at a conceptual level that is undiscerning to the underlying algorithms, representations, and their respective success.
  arXiv  Detail & Related papers  (2025-02-28T08:48:41Z)
• DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning [18.36413246876648]
  This work introduces an innovative method for estimating attention levels (cognitive load) using an ensemble of facial analysis techniques applied to webcam videos. Our approach adapts state-of-the-art facial analysis technologies to quantify the users' cognitive load in the form of high or low attention. Our method outperforms existing state-of-the-art accuracies using the public mEBAL2 benchmark.
  arXiv  Detail & Related papers  (2024-08-10T11:39:11Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias [17.518601254380275]
  We compare and rank eight user modeling algorithms based on their performance on a diverse set of four user study datasets. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.
  arXiv  Detail & Related papers  (2022-08-09T19:51:10Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Representation-Centric Survey of Skeletal Action Recognition and the ANUBIS Benchmark [43.00059447663327]
  3D skeleton-based human action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches.<n>Despite remarkable progress, current research remains fragmented across diverse input representations.<n>ANUBIS is a large-scale, challenging skeleton action dataset designed to address critical gaps in existing benchmarks.
  arXiv  Detail & Related papers  (2022-05-04T14:03:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.