Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis

TL;DR

This study investigates the increase in cyberbullying discussions on social media during COVID-19 using Bayesian trend analysis, revealing a correlation but not necessarily a rise in actual cyberbullying incidents.

Contribution

Introduces a Bayesian autoregressive Poisson model to analyze cyberbullying trend data, addressing serial correlation and small sample issues in social media analysis.

Findings

Upward trend in cyberbullying-related tweets since mid-March 2020

Bayesian method effectively detects trend changes in social media data

Correlation observed between COVID-19 crisis and cyberbullying discussions

Abstract

COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significant rise in cyberbullying. In this paper, we have examined the hypothesis of whether cyberbullying and reporting of such incidents have increased in recent times. To evaluate the speculations, we collected cyberbullying related public tweets (N=454,046) posted between January 1st, 2020 -- June 7th, 2020. A simple visual frequentist analysis ignores serial correlation and does not depict changepoints as such. To address correlation and a relatively small number of time points, Bayesian estimation of the trends is proposed for the collected data via an autoregressive Poisson model. We show that this new Bayesian method detailed in this paper can clearly show the upward trend on cyberbullying-related tweets since mid-March 2020. However, this evidence itself does not signify a rise in cyberbullying but shows a correlation of the crisis with the discussion of such incidents by individuals. Our work emphasizes a critical issue of cyberbullying and how a global crisis impacts social media abuse and provides a trend analysis model that can be utilized for social media data analysis in general.