Enhancing Free-Text Information Extraction For Improved Knowledge Acquisition and Retrieval from Social Media Networks

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ID: 312625
2025
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Abstract
The exponential growth of user-generated content on social media has led to an urgent need for intelligent systems that can extract structured knowledge from noisy, informal, and emotionally charged text. Traditional information extraction approaches often struggle with the nuances of free-text data, including code-switching, slang, sarcasm, and multimodal signals. This paper presents a novel framework that enhances free-text information extraction by integrating emotion-aware representation (EMO), multi-context attention mechanisms (MCAM), and multimodal fusion to bridge the gap between unstructured text and structured knowledge. Our approach jointly addresses named entity recognition, relation extraction, and knowledge retrieval from multimodal social media posts. Extensive experiments on benchmark datasets—MMHS-DS1, Hatebase-DSII, and HSOL-DSIII—demonstrate significant improvements over baseline models in F1 score, precision@1, and mean reciprocal rank (MRR). The proposed system also exhibits cross-domain robustness and provides a viable foundation for real-world applications such as hate speech detection, misinformation analysis, and dynamic knowledge graph population.
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imported_1777055423_69ebb6bf478e5 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Zeeshan
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1046
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