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Found 6 papers, 1 papers with code
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators.
On Analyzing Annotation Consistency in Online Abusive Behavior Datasets
In this study, we proposed an analytical framework to study the annotation consistency in online hate and abusive content datasets.
On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities.
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community.
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic
Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states.
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection
However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages.
