On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic

TL;DR

This paper presents a large annotated dataset of over 40 million COVID-19 related tweets to analyze online antisocial behaviors, revealing new abusive lexicons, vulnerable targets, and influencing factors during the pandemic.

Contribution

It introduces a novel annotation framework and provides empirical insights into the nature and spread of antisocial behaviors on social media during COVID-19.

Findings

New abusive lexicons emerged during COVID-19

Identified vulnerable targets of antisocial behaviors

Factors influencing the spread of antisocial content

Abstract

The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities. Such toxic behaviors take a heavy toll on society, especially during these daunting times. Despite the gravity of the issue, very few studies have studied online antisocial behaviors amid the COVID-19 pandemic. In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets. Specially, we propose an annotation framework to annotate the antisocial behavior tweets automatically. We also conduct an empirical analysis of our annotated dataset and found that new abusive lexicons are introduced amid the COVID-19 pandemic. Our study also identified the vulnerable targets of antisocial behaviors and the factors that influence the spreading of online antisocial content.