Papers with hate speech detection task

4 papers
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)

Copied to clipboard

Challenge: Hate speech detection depends on the availability of variable labeled data.
Approach: They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it.
Outcome: The proposed approach improves the classification performance of hate speech classification models.
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking (2024.findings-naacl)

Copied to clipboard

Challenge: Data augmentation techniques generate low-quality texts with incorrect labels . a new technique is needed to winnow out texts with inaccurate labels based on provenance inspection .
Approach: They develop a data inspection technique that uses provenance inspection and assistive labeling to winnow out texts with incorrect labels.
Outcome: a new human-in-the-loop data inspection technique can winnow out texts with incorrect labels . the technique can reduce human inspection effort by combining provenance inspection and assistive labeling .
Directions for NLP Practices Applied to Online Hate Speech Detection (2022.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to address hate speech in online spaces have relied on conventions and practices from NLP.
Approach: They argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.
Outcome: The proposed methods are poorly suited for the problem and should be adapted to address the propagation of online harms.
Disentangling Subjectivity and Uncertainty for Hate Speech Annotation and Modeling using Gaze (2025.emnlp-main)

Copied to clipboard

Challenge: Variation is inherent in opinion-based annotation tasks like sentiment or hate speech analysis.
Approach: They propose to use annotators' confidence ratings to disentangle subjective variation from uncertainty without relying on specific features present in the data.
Outcome: The proposed approach shows that human gaze patterns offer valuable indicators of subjective evaluation and uncertainty.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations