Induction of Knowledge, Attitude and Practice (KAP) of People Towards COVID-19 From Twitter Data: a Comprehensive Model Based on Opinion Mining and Deep Learning

Since the outbreak of the pandemic of coronavirus disease 2019 (COVID-19), a great number of studiesin the form of questionnaires have been made available to a study population to evaluate the publicknowledge, attitudes, and practices (KAP). This research aims to apply the data from the social media andemploy the text-processing approach to achieve similar results, apart from the questionnaires. For thepublic knowledge and practice, we labeled around a thousand cases on a random basis, based onprinciples of medical guidelines of the World Health Organization where each tweet can meet zero orseveral items of the hygiene guidelines and employed Krippendorff’s Alpha Coefficient and achievedinter-annotator agreement of 87%. For the public attitudes, we also similarly categorized 1000 instancesof viewpoints as positive, negative and neutral, where Krippendorff’s Alpha coefficient annotators’agreement of 95% has been achieved between labelers at this point. We evaluated XLNet and BERT asour proposed models with several machine learning and deep learning approaches. Consequently, XLNetachieved to be of the highest efficiency. The use of social network data can reveal the truth in the sensethat people freely and with their knowledge express their opinions, which can solely explore the publicpoints of interest and reduce the negligence of responses.

Induction of Knowledge, Attitude and Practice (KAP) of People Towards COVID-19 From Twitter Data: a Comprehensive Model Based on Opinion Mining and Deep Learning Keywords:

Text mining , Sentiment analysis , Deep learning , KAP , Cognitive science