Abstract
The proliferation of abnormal text information in social networks has become an important challenge for digital social governance. Traditional detection methods are unable to cope with increasingly complex semantic camouflage and dissemination strategies due to excessive reliance on one-dimensional analysis. Therefore, this research develops a detection method for abnormal text information in social networks that integrates the Graph-based User Interaction and Diffusion Evaluation (GUIDE) module and the Natural Language Ontology-driven Textual Anomaly Classification Engine (NOTICE) module. The GUIDE module captures anomalous propagation patterns through dynamic propagation tracking and network modeling, while the NOTICE module identifies semantic risks using a multilingual ontology library and deep semantic understanding. By combining structural and semantic analysis through a dual-attention fusion mechanism, the proposed framework simultaneously detects semantic anomalies and propagation topology anomalies, thereby improving detection accuracy and practicality. The experimental results show that the framework achieves F1 score of 91.2%, 89.7%, and 88.3% in detecting fake news, junk advertising, and hate speech tasks, respectively, which is 5.5–17.8 percentage points higher than the optimal baseline model. These evaluations are conducted on a comprehensive dataset from ZN Lab, containing real-world samples from major platforms like Twitter and Weibo. In actual deployment, the system maintains an accuracy rate of 89.4% when processing 230 million pieces of content per day, and reduces manual review by 43%. In terms of resource consumption, the memory usage remains stable at 645 MB and the response time is 76 ms, significantly better than traditional models. The above results indicate that the proposed model has excellent accuracy and applicability in detecting abnormal text information in social networks, effectively solving the problem of lack of accuracy and reliability in current detection methods. It provides an efficient and reliable technical solution for content governance on social platforms, especially in scenarios such as false information prevention and network violence governance, which has important application value.
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