Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models
This work addresses the challenge of moderating hate speech on social media platforms like Gab, offering improved detection methods for indirect conversations, though it is incremental in nature.
The authors tackled the problem of detecting hate speech in online social media by proposing a weak supervision deep learning model that scores content at the interaction level, outperforming baselines in identifying indirect hateful interactions on 19.2M posts, and using multilayer network embeddings to predict hateful users with up to a 7% performance gain.
The ubiquity of social media has transformed online interactions among individuals. Despite positive effects, it has also allowed anti-social elements to unite in alternative social media environments (eg. Gab.com) like never before. Detecting such hateful speech using automated techniques can allow social media platforms to moderate their content and prevent nefarious activities like hate speech propagation. In this work, we propose a weak supervision deep learning model that - (i) quantitatively uncover hateful users and (ii) present a novel qualitative analysis to uncover indirect hateful conversations. This model scores content on the interaction level, rather than the post or user level, and allows for characterization of users who most frequently participate in hateful conversations. We evaluate our model on 19.2M posts and show that our weak supervision model outperforms the baseline models in identifying indirect hateful interactions. We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users. We utilize the multilayer network embedding methods to generate features for the prediction task and we show that considering user context from multiple networks help achieving better predictions of hateful users in Gab. We receive up to 7% performance gain compared to single layer or homogeneous network embedding models.