Hate Speech Detection
164 papers with code • 14 benchmarks • 39 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.
Benchmarks
These leaderboards are used to track progress in Hate Speech Detection
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Libraries
Use these libraries to find Hate Speech Detection models and implementationsDatasets
Hate Speech
OLID
ToxiGen
Implicit Hate
Subtasks
Most implemented papers
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
Some users of social media are spreading racist, sexist, and otherwise hateful content.
Deep Learning for Hate Speech Detection in Tweets
pinkeshbadjatiya/twitter-hatespeech
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Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis.
Surfacing contextual hate speech words within social media
As an example, "skypes", "googles", and "yahoos" are all instances of words which have an alternate meaning that can be used for hate speech.
Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter
Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.
Examining a hate speech corpus for hate speech detection and popularity prediction
As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection.
Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models
This paper reports an increment to the state-of-the-art in hate speech detection for English-Hindi code-mixed tweets.
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text
bidishasamantakgp/2019_CSGen_ACL
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ACL 2019
Consequently, the best monolingual methods perform relatively poorly on code-switched text.
Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen.
