원문정보
초록
영어
As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as “Infodemic”. The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.
목차
1. Introduction
2. Literature Review
2.1 Misinformation Detection and Rectification Systems
2.2 Question Answer Generation
2.3 Open Domain Question Answering Model
2.4 Word-Embedding
2.5 Text Similarity
2.6 COVID-19 Datasets
3. Research Model and Implementation
3.1 Research Model
3.2 Model Implementation on Different Datasets
4. Results
4.1 Accuracy and F1 Score
4.2 Analysis Of Correctly/Falsely Answered Questions
5. Conclusion and Limitation
References