A computational approach in analyzing the empathy to online donations during COVID-19

Abstract

The COVID-19 pandemic has a negative impact on many aspects of life. The global economic downturn is one of these negative consequences. Nonetheless, even though everyone feels the threat of this pandemic for themselves, some people still have the empathy to help others. An empirical analysis of this empathy attitude is expected to be a catalyst in realizing a social force for the community to work together to combat this pandemic. This study will look at how people felt about donating during the COVID-19 pandemic on Twitter. The goals of this study are to (1) compare differences in donor desire before and during the COVID-19 pandemic using the developed model, and (2) determine whether there is a significant difference in empathy for donating before and during the pandemic. This study employs computational social science (CSS) techniques to achieve this goal. The data was obtained from Twitter using the keyword "donation" in the 24 months preceding the pandemic and in the 24 months following the pandemic's arrival in Indonesia. Data analysis includes hypothesis testing using Mann-Whitney and Cohen's D statistical tests, showing a significant increase in online donation support among Indonesian Twitter users since the COVID-19 pandemic hit. From the results of data processing data obtained 159.995 data in accordance with the criteria to be analyzed. From the results of the Mann-Whitney test, all variables showed significant results between before and during the Covid-19 pandemic and in the results of the Cohen's d test, all variables got a large effect size. From the results of the two tests, it can open Twitter social media users who have increased empathy to donate during the Covid-19 pandemic in Indonesia