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Found 7 papers, 2 papers with code
Challenges and frontiers in abusive content detection
Online abusive content detection is an inherently difficult task.
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking.
Detecting East Asian Prejudice on Social Media
The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Into the crossfire: evaluating the use of a language model to crowdsource gun violence reports
Here, we partner with a Brazilian human rights organization to conduct a systematic evaluation of language models to assist with monitoring real-world firearm events from social media data.
SemEval-2022 Task 8: Multilingual news article similarity
Thousands of new news articles appear daily in outlets in different languages.
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
