Papers with comparative analysis of
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. |
Human vs. Machine Perceptions on Immigration Stereotypes (2024.lrec-main)
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| Challenge: | a growing number of natural language processing models leave aside the language itself . a recent paradigm in the computational linguistics community is training models on specific perspectives of a segment of the population or an individual. |
| Approach: | They propose to use BERT-based classification models to detect stereotypes related to immigrants . they compare models with predictions from GPT-4 and annotated tweets from Spanish Twitter . |
| Outcome: | The proposed models are compared with predictions from the dataset of Spanish Twitter posts containing stereotypes . the models are confident in their predictions and more accurate for implicit stereotypes, the authors show . |