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Found 7 papers, 2 papers with code
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges
This survey presents a systematic and comprehensive exploration of Cross-Lingual Transfer Learning (CLTL) techniques in offensive language detection in social media.
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association.
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media
The goal of sexism detection is to mitigate negative online content targeting certain gender groups of people.
Cross-lingual Capsule Network for Hate Speech Detection in Social Media
Most hate speech detection research focuses on a single language, generally English, which limits their generalisability to other languages.
SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection
We propose the first Chinese sexism dataset -- Sina Weibo Sexism Review (SWSR) dataset --, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms.
Leveraging Aspect Phrase Embeddings for Cross-Domain Review Rating Prediction
Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, as well as they are valuable for other users and companies to find out the overall opinions of customers.
Early Detection of Social Media Hoaxes at Scale
Our dataset represents a realistic scenario with a real distribution of true, commemorative and false stories, which we release for further use as a benchmark in future research.
