An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2208.04980v1
• Date: Sat, 23 Jul 2022 15:24:07 GMT
• Title: An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic
• Authors: Christopher Perez, Sayar Karmakar
• Abstract summary: 1 million tweets containing keywords associated with cyberbullying were collected from the beginning of 2019 to the end of 2021. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. Results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data was also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.

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