Search queries related to COVID-19 based on keyword extraction.

ABSTRACT

Background: Pandemic COVID-19 caused an infodemic - massive spread of true and fake information about novel coronavirus. This study aims to present the possibility of using Keyword Extraction as a tool to obtain the most trending search queries related to COVID-19 and analyze the possibility of including their search volume in models for the prediction of fake news. Methods: The study used Python implementation of the machine learning-based technique KeyBERT to extract keywords from true and fake news. These keywords were used in the next step to obtain related search queries with Google Trends API. Results: Non-parametric Spearman Rank Order Correlation was identified as a statistically positive correlation (p < 0.001) between the occurrence of false news and top query / rising query metrics provided by Google Trends of queries related to extracted keywords pandemic, HIV, lockdown, plague, Michigan, and protest, which proves that search volume can identify fake news. Conclusions: Experiments done in this research proved that Keyword Extraction from false news is useful for obtaining related search queries and the top query and rising query metrics can be used to increase the accuracy of fake news prediction models.