Fake Information Detection Method Based on BERT Pre-Trained Model

With the rapid evolution and ubiquitous penetration of social media, the proliferation of false information-characterized by rapid diffusion and contextual complexity-has become a critical societal challenge, distorting public cognition in domains like public health and electoral politics, and threa...

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Bibliographic Details
Published in:2025 IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA) pp. 1627 - 1630
Main Authors: Wang, Xu, Liu, Sitong, Lu, Shige, Bi, Yunqi
Format: Conference Proceeding
Language:English
Published: IEEE 28.06.2025
Subjects:
BERT
Bidirectional control
Biological system modeling
Blogs
Context modeling
Deep Learning
Encoding
Fake Information Detection
Fake news
Semantics
Text categorization
Text Classification
Text recognition
Transformers
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Summary:With the rapid evolution and ubiquitous penetration of social media, the proliferation of false information-characterized by rapid diffusion and contextual complexity-has become a critical societal challenge, distorting public cognition in domains like public health and electoral politics, and threatening social cohesion or even triggering unrest in extreme cases. Traditional detection methods, such as keyword matching or shallow machine learning models, often struggle to capture semantic ambiguity and contextual dependencies. To address this, this study proposes a refined false information detection framework based on BERT, leveraging its strengths in deep semantic parsing and dynamic contextual representation. The framework integrates multi-dimensional text preprocessing (e.g., noise reduction and entity recognition) with a pre-trained fine-tuning paradigm to capture nuanced semantic associations critical for distinguishing false information, avoiding information loss from manual feature engineering. Experimental results on the Weibo dataset demonstrate that the proposed method achieves relatively advanced performance across core evaluation metrics. Moreover, it maintains good stability under data distribution shifts and adversarial perturbations, highlighting its potential for deployment in real-world applications.
DOI:10.1109/ICIPCA65645.2025.11138502