Understanding the Rise of Twitter-based cyberbullying due to COVID-19 through comprehensive statistical evaluation

Date
2021-01-05
Authors
Karmakar, Sayar
Das, Sanchari
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
2521
Ending Page
Alternative Title
Abstract
The COVID-19 pandemic has created a challenging situation for everyone, sparking digital evolution due to stay-at-home restrictions to stop the spread. This has led to an uprise of digital presence, which many hypothesize has lead to a rise of cybersecurity attacks, including cyberbullying. To evaluate the significance of COVID-19 on cyberbullying reports, we collected 454, 046 of publicly available tweets from Twitter by using MongoDB and Python libraries from January 1st, 2020–June 7th, 2020. We performed statistical analyses on the collected sample set to understand the situation from a quantitative perspective. We extracted tweets related to 27 unique keywords specific to cyberbullying, including online bullying, cyberbullying, Twitter bullying, and others. Due to the time-series’ count nature, we propose a Bayesian estimation of this count data trends utilizing an autoregressive Poisson model. A simple change-point model fails to explain the subtle changes adequately. On the other hand, our Bayesian method clearly shows the upward trend beginning in mid-March, which is reportedly the time from which the stay-at-home orders were widespread globally. The pattern remains similar if we focus on one or more such keywords instead of the total count. We also provide a fine-grained lag based analysis of our model and contrast our methods with an alternative semi-Bayesian AR-ARCH model. Overall, such analysis shows somewhat conclusive evidence of the rise around the same time as COVID.
Description
Keywords
Big Data-driven Social Media Management, bayesian analysis, covid-19, cyberbully, social media research, trend analysis, twitter
Citation
Extent
11 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.