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COVID-19 has brought about many changes in social dynamics. Stay-at-homeorders and disruptions in school teaching can influence bullying behaviorin-person and online, both of which leading to negative outcomes in victims. Tostudy cyberbullying specifically, 1 million tweets containing keywordsassociated with abuse were collected from the beginning of 2019 to the end of2021 with the Twitter API search endpoint. A natural language processing modelpre-trained on a Twitter corpus generated probabilities for the tweets beingoffensive and hateful. To overcome limitations of sampling, data was alsocollected using the count endpoint. The fraction of tweets from a given dailysample marked as abusive is multiplied to the number reported by the countendpoint. Once these adjusted counts are assembled, a Bayesian autoregressivePoisson model allows one to study the mean trend and lag functions of the dataand how they vary over time. The results reveal strong weekly and yearlyseasonality in hateful speech but with slight differences across years that maybe attributed to COVID-19.

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