Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70923

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

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Title:Understanding the Rise of Twitter-based cyberbullying due to COVID-19 through comprehensive statistical evaluation
Authors:Karmakar, Sayar
Das, Sanchari
Keywords:Big Data-driven Social Media Management
bayesian analysis
covid-19
cyberbully
social media research
show 2 moretrend analysis
twitter
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Date Issued:05 Jan 2021
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.
Pages/Duration:11 pages
URI:http://hdl.handle.net/10125/70923
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.309
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data-driven Social Media Management


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