CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network

Although pretrained large language models (PLMs) have achieved state of the art on many natural language processing tasks, they lack an understanding of subtle expressions of implicit hate speech. Various attempts have been made to enhance the detection of implicit hate by augmenting external context or enforcing label separation via distance-based metrics. Combining these two approaches, we introduce FiADD, a novel focused inferential adaptive density discrimination framework. FiADD enhances the PLM finetuning pipeline by bringing the surface form/meaning of an implicit hate speech closer to its implied form while increasing the intercluster distance among various labels. We test FiADD on three implicit hate datasets and observe significant improvement in the two-way and three-way hate classification tasks. We further experiment on the generalizability of FiADD on three other tasks, detecting sarcasm, irony, and stance, in which surface and implied forms differ, and observe similar performance improvements. Consequently, we analyze the generated latent space to understand its evolution under FiADD, which corroborates the advantage of employing FiADD for implicit hate speech detection.

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Hate speech detection in the Bengali language: a comprehensive survey

Al Maruf,

Abidin,

Haque

et al. 2024

J Big Data

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The detection of hate speech (HS) in online platforms has become extremely important for maintaining a safe and inclusive environment. While significant progress has been made in English-language HS detection, methods for detecting HS in other languages, such as Bengali, have not been explored much like English. In this survey, we outlined the key challenges specific to HS detection in Bengali, including the scarcity of labeled datasets, linguistic nuances, and contextual variations. We also examined different approaches and methodologies employed by researchers to address these challenges, including classical machine learning techniques, ensemble approaches, and more recent deep learning advancements. Furthermore, we explored the performance metrics used for evaluation, including the accuracy, precision, recall, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, specificity, and F1 score, providing insights into the effectiveness of the proposed models. Additionally, we identified the limitations and future directions of research in Bengali HS detection, highlighting the need for larger annotated datasets, cross-lingual transfer learning techniques, and the incorporation of contextual information to improve the detection accuracy. This survey provides a comprehensive overview of the current state-of-the-art HS detection methods used in Bengali text and serves as a valuable resource for researchers and practitioners interested in understanding the advancements, challenges, and opportunities in addressing HS in the Bengali language, ultimately assisting in the creation of reliable and effective online platform detection systems.

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CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network

Cited by 3 publications

References 39 publications

Unveiling the Hate: Generating Faithful and Plausible Explanations for Implicit and Subtle Hate Speech Detection

Unveiling the Hate: Generating Faithful and Plausible Explanations for Implicit and Subtle Hate Speech Detection

Focal inferential infusion coupled with tractable density discrimination for implicit hate detection

Hate speech detection in the Bengali language: a comprehensive survey

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