Papers by Nicolas Ocampo
An In-depth Analysis of Implicit and Subtle Hate Speech Messages (2023.eacl-main)
Copied to clipboard
| 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 . |
| Approach: | They propose to use a fine-grained definition of implicit and subtle messages to detect HS . they then experiment with neural network architectures to detect subtle content . |
| Outcome: | The proposed models perform satisfactory on explicit messages, but fail to detect subtle content. |
Playing the Part of the Sharp Bully: Generating Adversarial Examples for Implicit Hate Speech Detection (2023.findings-acl)
Copied to clipboard
| 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. |
| Approach: | They propose a framework for generating adversarial implicit HS short-text messages using Auto-regressive language models and a strategy to group the generated messages in complexity levels. |
| Outcome: | The proposed framework shows that iteratively retraining on HARD messages significantly improves implicit HS benchmarks. |
Unmasking the Hidden Meaning: Bridging Implicit and Explicit Hate Speech Embedding Representations (2023.findings-emnlp)
Copied to clipboard
| 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. |