Large-Scale Twitter Mining for Extracting the Psychological Impacts of COVID-19

The outbreak of the COVID-19 in 2020 and lack of an effective cure caused psychological problems among humans. This has been reflected widely on social media. Analyzing a large number of English tweets posted in the early stages of the pandemic, this paper addresses three psychological parameters: fear, hope, and depression. The main issue is the extraction of the related tweets with each of these parameters. To this end, three lexicons are proposed for these psychological parameters to extract the tweets through content analysis. A lexicon-based method is then used with GEO Names (i.e. a geographical database) to label tweets with country tags. Fear, hope, and depression trends are then extracted for the entire world and 30 countries. According to the analysis of results, there is a high correlation between the frequency of tweets and the official daily statistics of active cases in many countries. Moreover, fear tweets dominate hope tweets in most countries, something which shows the worldwide fear in the early months of the pandemic. Ultimately, the diagrams of many countries demonstrate unusual spikes caused by the dissemination of specific news and announcements.