Unmasking the Hidden Meaning: Bridging Implicit and Explicit Hate Speech Embedding Representations (2023.findings-emnlp)
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| Challenge: | Existing methods to detect explicit hate speech (HS) are focusing on detecting explicit forms of hateful expressions on user-generated content. |
| Approach: | They propose to examine the differences between embedding implicit and explicit hateful messages . they compare and link explicit and implicit hateful message across datasets . |
| Outcome: | The proposed model improves on explicit hate speech detection while retaining high performance on borderline cases. |
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| Challenge: | Explicit hate speech is more easily identifiable by recognizing hateful words, but subtle messages are harmful . subtle messages contain linguistically subtle and implicit forms of HS, such as circumlocution, metaphors and sarcasm . social media have faced pressure from civil rights groups demanding to monitor and limit online hate speech . |
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Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang
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| Challenge: | Existing algorithms for hate speech detection focus on explicit forms of hate speech, but they fail to properly detect subtle and implicit HS messages. |
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
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| Challenge: | Imlicit hate content has unusual syntax, polysemic words, and fewer markers of prejudice, e.g., slurs . multimodal content is harder to detect than unimodal content, such as memes . |
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| Challenge: | Hate speech detection is challenging when there are insufficient lexical cues. |
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| Challenge: | Hate speech classifiers do not perform equally well in detecting hateful expressions towards different target identities. |
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When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (2026.eacl-short)
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| Challenge: | Existing methods for hate speech detection treat hate speech as a monolithic phenomenon, ignoring the speaker’s motivations and potential societal consequences. |
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AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection (2025.emnlp-main)
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| Challenge: | Current approaches to detect hate speech rely on contrastive learning to distinguish hate from non-hate sentences. |
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ConPrompt: Pre-training a Language Model with Machine-Generated Data for Implicit Hate Speech Detection (2023.findings-emnlp)
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| Challenge: | Existing pre-trained language models for hate speech detection are not specialized in implicit hate speech. |
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