Work

Can Contextualizing User Embeddings Improve Sarcasm and Hate Speech Detection?

Year: 2022

Type: conference-paper

Abstract: While implicit embeddings so far have been mostly concerned with creating an overall representation of the user, we evaluate a different approach.By only considering content directed at a specific topic, we create sub-user embeddings, and measure their usefulness on the tasks of sarcasm and hate speech detection.In doing so, we show that task-related topics can have a noticeable effect on model performance, especially when dealing with intended expressions like sarcasm, but less so for hate speech, which is usually labelled as such on the receiving end.

Source: Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)

DOI: 10.18653/v1/2022.nlpcss-1.14

Author: Kim Breitwieser

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4385573946

FWCI: 0.138

Cites: 57

Cited by: 2

Related to: 10