"Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning

TL;DR

This paper introduces the first multimodal deep learning approach for detecting online antisemitism, addressing a critical gap by creating new datasets and evaluating system effectiveness across social media platforms.

Contribution

It presents a novel multimodal deep learning system for antisemitism detection, along with the creation and labeling of two new benchmark datasets from Twitter and Gab.

Findings

Effective detection of antisemitic content using text and images

Demonstrated system's high accuracy on new datasets

Provided qualitative insights into antisemitic content patterns

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.