Open AccessPosted Content
Interpretable Multi-Modal Hate Speech Detection.
TLDR
This article proposed a deep neural multi-modal model that can detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and provide interpretable insights into decisions of the model.Abstract:
With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works have proposed automated techniques to detect hate speech online, these techniques primarily fail to look beyond the textual content. Moreover, few attempts have been made to focus on the aspects of interpretability of such models given the social and legal implications of incorrect predictions. In this work, we propose a deep neural multi-modal model that can: (a) detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and (b) provide interpretable insights into decisions of our model. By performing a thorough evaluation of different modeling techniques, we demonstrate that our model is able to outperform the existing state-of-the-art hate speech classification approaches. Finally, we show the importance of social and cultural context features towards unearthing clusters associated with different categories of hate.read more
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The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Douwe Kiela,Hamed Firooz,Aravind Mohan,Vedanuj Goswami,Amanpreet Singh,Pratik Ringshia,Davide Testuggine +6 more
TL;DR: The authors proposed a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, where difficult examples are added to the dataset to make it hard to rely on unimodal signals.
Proceedings ArticleDOI
Impact of politically biased data on hate speech classification
TL;DR: It is shown that political bias negatively impairs the performance of hate speech classifiers and an explainable machine learning model can help to visualize such bias within the training data.
Proceedings ArticleDOI
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
TL;DR: This work reveals that user features play a role in the model’s decision and how they affect the feature space learned by the model, and shows how such techniques can be combined to better understand the model and to detect unintended bias.
Book ChapterDOI
Explainable Abusive Language Classification Leveraging User and Network Data
TL;DR: In this article, an explainable AI framework SHAP (SHapley Additive explanations) is proposed to alleviate the general issue of missing transparency associated with deep learning models, allowing the model to assess the model's vulnerability toward bias and systematic discrimination reliably.
Proceedings ArticleDOI
A Multimodal Deep Framework for Derogatory Social Media Post Identification of a Recognized Person
TL;DR: In this article, social media platforms play a significant role in networking and influencing the perception of the general population in today's era of digitization, and social network sites have recently been used t...
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