School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.
Artif Intell Med. 2023 Sep;143:102627. doi: 10.1016/j.artmed.2023.102627. Epub 2023 Jul 24.
Coronavirus (COVID-19) is a newly discovered viral disease from the SARS-CoV-2 family. This has caused a moral panic resulting in the spread of informative and uninformative information about COVID-19 and its effects. Twitter is a popular social media platform used extensively during the current outbreak. This paper aims to predict informative tweets related to COVID-19 on Twitter using a novel set of fuzzy rules involving deep learning techniques. This study focuses on identifying informative tweets during the pandemic to provide the public with trustworthy information and forecast how quickly diseases could spread. In this case, we have implemented RoBERTa and CT-BERT models using the fuzzy methodology to identify COVID-19 patient tweets. The proposed architecture combines deep learning transformer models RoBERTa and CT-BERT with the fuzzy technique to categorize posts as INFORMATIVE or UNINFORMATIVE. We performed a comparative analysis of our method with machine learning models and deep learning approaches. The results show that our proposed model can classify informative and uninformative tweets with an accuracy of 91.40% and an F1-score of 91.94% using the COVID-19 English tweet dataset. The proposed model is accurate and ready for real-world application.
冠状病毒（COVID-19）是一种来自 SARS-CoV-2 家族的新发现的病毒性疾病。这导致了道德恐慌，导致 COVID-19 及其影响的信息和非信息传播。Twitter 是一个广受欢迎的社交媒体平台，在当前疫情期间被广泛使用。本文旨在使用涉及深度学习技术的一组新的模糊规则来预测 Twitter 上与 COVID-19 相关的信息性推文。本研究专注于识别大流行期间的信息性推文，为公众提供可靠的信息，并预测疾病传播的速度。在这种情况下，我们使用模糊方法实现了 RoBERTa 和 CT-BERT 模型，以识别 COVID-19 患者推文。所提出的架构将深度学习转换器模型 RoBERTa 和 CT-BERT 与模糊技术相结合，将帖子分类为信息性或非信息性。我们对我们的方法与机器学习模型和深度学习方法进行了比较分析。结果表明，我们提出的模型可以使用 COVID-19 英语推文数据集以 91.40%的准确率和 91.94%的 F1 分数对信息性和非信息性推文进行分类。该模型准确，可用于实际应用。
