Constructing an Anti-Asian Hate Indicator for Pandemic-related Comments from Mainstream Media YouTube Channels
Wang X., Chen X., Bolian L. & Zhao P. (2022). "Constructing an Anti-Asian Hate Indicator for Pandemic-related Comments from Mainstream Media YouTube Channels." International Journal of Society Systems Science in Publishing
Anti-Asian hate indicator; YouTube comments; Mainstream media channels; BERT embedding; Support vector machine; Random forest; LSTM; CNN; YouTube Data API
Abstract: Anti-Asian racism, linked to COVID-19, has become a serious social problem in the United States and all over the world and even led to hate crime and violence. Even though the current anti-Asian hate study focuses anti-Asian hate classification using machine learning and sentiment analysis toward tweets, this study provides a novel pandemic-news-related anti-Asian hate indicator to depict the anti-Asian hate shift of YouTube mainstream media commentary section. A new dataset for daily hate signal generation, which contains over 1 million YouTube comments, has been generated in this study. To train the classifier, 3,759 comments are sampled and manually labelled as hate and non-hate. In the model selection among machine learning and deep learning algorithms, a CNN model is selected as the best one with a 95% accuracy and a 0.99 AUC score, which can classify 1,433,246 comments.