“Subverting the Jewtocracy”: Online Antisemitism Detection Using Multimodal Deep Learning

Published in 13th ACM Web Science Conference (WebSci’21), 2021

Abstract: 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.

Recommended Citation: Mohit Chandra, Dheeraj Pailla, Himanshu Bhatia, Aadilmehdi Sanchawala, Manish Gupta, Manish Shrivastava, and Ponnurangam Kumaraguru. 2021. “Subverting the Jewtocracy”: Online Antisemitism Detection Using Multimodal Deep Learning. In 13th ACM Web Science Conference 2021 (WebSci’21), June 21–25, 2021, Virtual Event, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3447535.3462502


Download paper: here