Working Paper Article Version 1 This version is not peer-reviewed

Multimodal Hate Speech Detection in Greek Social Media

Version 1 : Received: 13 March 2021 / Approved: 15 March 2021 / Online: 15 March 2021 (13:46:27 CET)

How to cite: Perifanos, K.; Goutsos, D. Multimodal Hate Speech Detection in Greek Social Media. Preprints 2021, 2021030390 Perifanos, K.; Goutsos, D. Multimodal Hate Speech Detection in Greek Social Media. Preprints 2021, 2021030390

Abstract

Hateful and abusive speech presents a major challenge for all online social media platforms. Recent advances in Natural Language Processing and Natural Language Understanding allow more accurate detection of hate speech in textual streams. This study presents a multimodal approach to hate speech detection by combining Computer Vision and Natural Language processing models for abusive context detection. Our study focuses on Twitter messages and, more specifically, on hateful, xenophobic and racist speech in Greek aimed at refugees and migrants. In our approach we combine transfer learning and fine-tuning of Bidirectional Encoder Representations from Transformers (BERT) and Residual Neural Networks (Resnet). Our contribution includes the development of a new dataset for hate speech classification, consisting of tweet ids, along with the code to obtain their visual appearance, as they would have been rendered in a web browser. We have also released a pre-trained Language Model trained on Greek tweets, which has been used in our experiments. We report a consistently high level of accuracy (accuracy score=0.970, f1-score=0.947 in our best model) in racist and xenophobic speech detection.

Subject Areas

Multimodal Machine Learning; Deep Learning; Hate Speech Detection

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