Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

Liam, Hebert,; Golab, Lukasz; Cohen, Robin

doi:10.1109/wi-iat55865.2022.00012

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…They also propose the first dataset, Latent Hatred, to benchmark model performance on implicit hate speech classification and show that existing stateof-the-art classifiers fail to perform well on the benchmark. Though Latent Hatred builds on an exhaustive 6-class taxonomy, it ignores implicit hate speech that is conversational-context-sensitive even though it accounts for a majority of implicit hate speech online (Modha et al, 2022;Hebert et al, 2022). Lin (2022) builds on Latent Hatred and propose one of the first systems to classify implicit hate speech leveraging world knowledge through knowledge graphs (KGs).…”

Section: Non-hatefulmentioning

confidence: 99%

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

Ghosh,

Suri,

Chiniya

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech affecting people from various demographics. Most of the prior works focus on detecting explicit hate speech, which is overt and leverages hateful phrases, with very little work focusing on detecting hate speech that is implicit or denotes hatred through indirect or coded language. In this paper, we present CoSyn, a context synergized neural network that explicitly incorporates user-and conversational-context for detecting implicit hate speech in online conversations. CoSyn introduces novel ways to encode these external contexts and employs a novel context interaction mechanism that clearly captures the interplay between them, making independent assessments of the amounts of information to be retrieved from these noisy contexts. Additionally, it carries out all these operations in the hyperbolic space to account for the scalefree dynamics of social media. We demonstrate the effectiveness of CoSyn on 6 hate speech datasets and show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% -57.8%. We make our code available 1 .

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Section: Non-hatefulmentioning

confidence: 99%

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

Ghosh,

Suri,

Chiniya

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

Acharya,

Das,

Sudarshan

2024

IEEE Access

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Metaphorical memes, where a source concept is projected into a target concept, are an essential construct in figurative language. In this article, we present a novel approach for downstream learning tasks on metaphorical multimodal memes. Our proposed framework replaces traditional methods using metaphor annotations with a metaphor-capturing mechanism. Besides using the significant zero-shot learning capability of state-of-the-art pretrained encoders, this work introduces an alternative external knowledge enhancement strategy based on ChatGPT (chatbot generative pretrained transformer), demonstrating its effectiveness in bridging the intermodal semantic gap. We propose a new concept projection process consisting of three distinct components to capture the intramodal knowledge and intermodal concept gap in the forms of text modality embedding, visual modality embedding, and concept projection embedding. This approach leverages the attention mechanism of the Graph Attention Network for fusing the common aspects of external knowledge related to the knowledge in the text and image modality to implement the concept projection process. Our experimental results demonstrate the superiority of our proposed approach compared to existing methods.

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Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

Cited by 2 publications

References 9 publications

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

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

Capturing the Concept Projection in Metaphorical Memes for Downstream Learning Tasks

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