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

arXiv: https://arxiv.org/abs/2208.04980 · HTML: https://mlsanity.com/paper/2208.04980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)
- [Impact of the COVID-19 pandemic on the Internet latency: a large-scale study](https://mlsanity.com/paper/2005.06127/) (2020)
- [Analyzing initial stage of COVID-19 transmission through Bayesian time-varying model](https://mlsanity.com/paper/2004.02281/) (2020)
- [Change-Point Analysis of Cyberbullying-Related Twitter Discussions During COVID-19](https://mlsanity.com/paper/2008.13613/) (2020)
- [Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis](https://mlsanity.com/paper/2009.02126/) (2020)

## Built on by (most cited first, 1 of 1)

- [Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety](https://mlsanity.com/paper/2609.09735/) (2026)

## Abstract (arXiv)

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.