On Analyzing COVID-19-related Hate Speech Using BERT Attention

• Published in: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 669 - 676
• Main Authors: Vishwamitra, Nishant, Hu, Ruijia Roger, Luo, Feng, Cheng, Long, Costello, Matthew, Yang, Yin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2020
• Subjects: Analytical models; BERT; Bit error rate; Blogs; COVID-19; explanation; hate-speech; online-hate; Predictive models; Social networking (online); Training data; Twitter
• DOI: 10.1109/ICMLA51294.2020.00111

The emergence of COVID-19 has engendered a new wave of online hate speech in social media platforms such as Twitter. Its widespread effects range from acts of cyber-harassment towards certain ethnic communities (e.g., the Asian community), to targeting older people belonging to age groups correlated with higher mortality rates (termed infamously as "Boomer Remover"). Thus, an urgent need arises for a timely mitigation of this new wave of online hate speech. In this work, we aim to discover the hate-related keywords linked to COVID-19 in hateful tweets posted on Twitter so that users posting such keywords can be asked to reconsider posting them. We first collect a new dataset of tweets targeting older people supplementing with a dataset targeting the Asian community. Then, we develop an approach to analyze the datasets with BERT (a transformer-based model) attention mechanism and discover 186 novel keywords targeting the Asian community and 100 keywords targeting older people. Based on our study, we then propose a control mechanism wherein a user can be asked to reconsider using certain sensitive words identified by our approach. We further perform an exploratory analysis of BERT attention mechanism and find that the most high-impact, long distance attentions are learned in the earlier or later layers of the model depending on the underlying data distribution. Our study indicates that the BERT model in some cases uses a hate keyword and an associated group or individual to make predictions, a finding that is inline with existing hate-speech research, which suggests that hate-speech is often aimed at certain groups or individuals.