Hate Speech Detection
159 papers with code • 14 benchmarks • 38 datasets
Hate speech detection is the task of detecting if communication such as text, audio, and so on contains hatred and or encourages violence towards a person or a group of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age et al. Some example benchmarks are ETHOS and HateXplain. Models can be evaluated with metrics like the F-score or F-measure.
Libraries
Use these libraries to find Hate Speech Detection models and implementationsDatasets
Subtasks
Most implemented papers
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers).
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
We provide a new dataset of ~40, 000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation.
A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts
On social medias, hate speech has become a critical problem for social network users.
Detecting Hate Speech with GPT-3
Given this capacity, we are interested in whether large language models can be used to identify hate speech and classify text as sexist or racist.
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset
This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset.
Few-shot Learning with Multilingual Language Models
Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
Attentive Fusion: A Transformer-based Approach to Multimodal Hate Speech Detection
With the recent surge and exponential growth of social media usage, scrutinizing social media content for the presence of any hateful content is of utmost importance.