Event-driven timeseries analysis and the comparison of public reactions on COVID-19

TL;DR

This study analyzes public reactions to COVID-19 measures across four countries using event-driven timeseries and machine learning, revealing varied impacts and validating findings with manual and classification methods.

Contribution

It introduces a combined rule-based and machine learning approach to analyze public sentiment around COVID-19 events across multiple countries.

Findings

Positive impacts of lockdown and economic support observed in UK and Australia

Opposite effects detected in Japanese public reactions

Supervised logistic regression achieved 83.11% accuracy in classifying health and economic worries

Abstract

The rapid spread of COVID-19 has already affected human lives throughout the globe. Governments of different countries have taken various measures, but how they affected people lives is not clear. In this study, a rule-based and a machine-learning based models are applied to answer the above question using public tweets from Japan, USA, UK, and Australia. Two polarity timeseries (meanPol and pnRatio) and two events, namely "lockdown or emergency (LED)" and "the economic support package (ESP)", are considered in this study. Statistical testing on the sub-series around LED and ESP events showed their positive impacts to the people of (UK and Australia) and (USA and UK), respectively unlike Japanese people that showed opposite effects. Manual validation with the relevant tweets showed an agreement with the statistical results. A case study with Japanese tweets using supervised logistic regression classifies tweets into heath-worry, economy-worry and other classes with 83.11% accuracy. Predicted tweets around events re-confirm the statistical outcomes.