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.
翻译:COVID-19在社会动态方面带来了许多变化。 留在家中的命令和学校教学的中断会影响亲身和在线的欺凌行为,两者都会导致受害者遭受负面后果。 要具体研究网络欺凌,从2019年初到2021年底,通过Twitter API搜索终点收集了100万条含有与虐待相关关键词的推特。在推特上预先培训的自然语言处理模型产生了推文攻击和仇恨的概率。为了克服抽样限制,还利用计数端点收集了数据。每天标注为虐待的样本中的推文数量与计数端点报告的数量成倍。一旦这些调整的计数组完成,巴伊斯的自反波索模式允许人们研究数据的平均趋势和滞后功能及其随时间变化的情况。结果显示,仇恨性言论每周和每年的季节性都有很强的差别,可能归因于COVID-19。</s>