Event-driven timeseries analysis and the comparison of public reactions on COVID-19
This work addresses how government COVID-19 policies affected public sentiment in specific countries, but it is incremental as it applies existing methods to new data.
The study analyzed public reactions to COVID-19 measures like lockdowns and economic support using tweets from Japan, USA, UK, and Australia, finding positive impacts in some countries (e.g., UK and Australia for lockdowns) but opposite effects in Japan, with a case study achieving 83.11% accuracy in tweet classification.
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.