"Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning
This work addresses a critical gap in automated detection of online antisemitism, a form of abuse with socio-political consequences, by introducing the first multimodal approach and new datasets for this domain-specific task.
The paper tackles the problem of detecting online antisemitism, which lacks prior machine learning studies, by collecting two new datasets from Twitter and Gab and presenting a multimodal deep learning system that detects antisemitic content and its categories using text and images.
The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.