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Found 8 papers, 5 papers with code
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
As hate speech spreads on social media and online communities, research continues to work on its automatic detection.
Impact of Politically Biased Data on Hate Speech Classification
One challenge that social media platforms are facing nowadays is hate speech.
Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics
To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators.
Investigating Annotator Bias with a Graph-Based Approach
In the scope of this study, we want to investigate annotator bias — a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception.
Investigating Annotator Bias in Abusive Language Datasets
A prevalent form of bias in hate speech and abusive language datasets is annotator bias caused by the annotator’s subjective perception and the complexity of the annotation task.
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators.
Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community
Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate users content.
