Combating the COVID-19 infodemic using Prompt-Based curriculum learning

• Published in: Expert systems with applications Vol. 229; p. 120501
• Main Authors: Peng, Zifan, Li, Mingchen, Wang, Yue, Ho, George T.S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2023
• Subjects: COVID-19; Curriculum learning; Deep learning; Social media; Text mining; Text mining; Curriculum learning; CNN; LN; GELU; Social media; NLI; SVM; BiLSTM; MHSA; PLM; COVID-19; Deep learning; NLP; RNN; BERT
• DOI: 10.1016/j.eswa.2023.120501
• ISSN: 0957-4174

The COVID-19 pandemic has been accompanied by a proliferation of online misinformation and disinformation about the virus. Combating this ‘infodemic’ has been identified as one of the top priorities of the World Health Organization, because false and misleading information can lead to a range of negative consequences, including the spread of false remedies, conspiracy theories, and xenophobia. This paper aims to combat the COVID-19 infodemic on multiple fronts, including determining the credibility of information, identifying its potential harm to society, and the necessity of intervention by relevant organizations. We present a prompt-based curriculum learning method to achieve this goal. The proposed method could overcome the challenges of data sparsity and class imbalance issues. Using online social media texts as input, the proposed model can verify content from multiple perspectives by answering a series of questions concerning the text’s reliability. Experiments revealed the effectiveness of prompt tuning and curriculum learning in assessing the reliability of COVID-19-related text. The proposed method outperforms typical text classification methods, including fastText and BERT. In addition, the proposed method is robust to the hyperparameter settings, making it more applicable with limited infrastructure resources.