Research on Identification of Health Misinformation on Social Media Based on RoBERTa-MHA-BiGRU

Abstract

Abstract: Health misinformation on social media is intricate fast-spreading and highly harmful topublic health. Rapid and effective identification of such misinformation is thus of great importance. Inthis study we first gathered health information from various social media platforms to build a bilingualdataset. We then developed a RoBERTa-MHA-BiGRU model to identify health misinformation. In thismodel the RoBERTa a pre-trained language model was used to vectorize the health data combining amulti-head attention mechanism with a bidirectional gated recurrent units BiGRU to extract semanticfeatures from the texts. And a fully connected layer and the Softmax function were employed to identifyhealth misinformation. Finally three sets of experiments were conducted for the Chinese and Englishdatasets to validate the effectiveness of the RoBERTa-MHA-BiGRU model. Experiment 1 showed thatdeep learning models outperformed machine learning models and that RoBERTa's text representation wassuperior to BERT􀆳s. Experiment 2 demonstrated that incorporating an attention mechanism enhanced themodel's learning capabilities with the RoBERTa-MHA-BiGRU model outperforming the single-headattention model. Experiment 3 revealed that data augmentation further improved the model sperformance. In summary this paper expands the depth and breadth of theoretical research on healthmisinformation. Practically it provides technical guidance for the identification of health misinformationon social media helping social media users to avoid such misinformation promptly and improving the efficiency and effectiveness of health misinformation management.

Key words: Health misinformation, Social media, Multi-head attention mechanisms, BiGRU, Dataaugmentation

Chen Minghong, He Jianing (School of Information Management Sun Yat-sen University). Research on Identification of Health Misinformation on Social Media Based on RoBERTa-MHA-BiGRU[J]. Libraly Journal, 2025, 44(416): 81-92.

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