Coronavirus statistics causes emotional bias: a social media text mining perspective (2211.08644v1)

Abstract: While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have confirmed that the number of COVID-19 cases can cause negative emotions, but how statistics of different dimensions, such as the number of imported cases, the number of local cases, and the number of government-designated lockdown zones, stimulate people's emotions requires detailed understanding. In order to obtain the views of people on COVID-19, this paper first proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis. The performance of the algorithm shows a promising result. The empirical study demonstrates while the number of local cases is positively associated with risk perception, the number of imported cases is negatively associated with confidence levels, which explains why citizens tend to ascribe the protracted pandemic to foreign factors. Besides, this study finds that previous pandemic hits cities recover slowly from the suffering, while local governments' spending on healthcare can improve the situation. Our study illustrates the reasons for risk perception and confidence based on different sources of statistical information due to cognitive bias. It complements the knowledge related to epidemic information. It also contributes to a framework that combines sentiment analysis using advanced deep learning technology with the empirical regression method.