专题：Hate Speech and Cyberbullying Detection

This cluster of papers focuses on the automated detection of hate speech, offensive language, and cyberbullying in social media platforms such as Twitter. It explores various techniques including machine learning, natural language processing, and deep learning to identify and categorize abusive content, with a specific emphasis on mitigating online harassment and promoting online safety.
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Taxonomy of Risks posed by Language Models

conference-paper Full Text OpenAlex 746 FWCI125.3508

Bias and Fairness in Large Language Models: A Survey

article Full Text OpenAlex 736 FWCI296.2836

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

article Full Text OpenAlex 573 FWCI35.3449

Sentiment Analysis and Opinion Mining

reference-entry Full Text OpenAlex 518 FWCI0

Cross-modal Ambiguity Learning for Multimodal Fake News Detection

conference-paper Full Text OpenAlex 397 FWCI229.0574

Gender bias and stereotypes in Large Language Models

conference-paper Full Text OpenAlex 380 FWCI95.2947

Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges

article Full Text OpenAlex 367 FWCI31.9248

Biases in Large Language Models: Origins, Inventory, and Discussion

article Full Text OpenAlex 363 FWCI87.4671

RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network

article Full Text OpenAlex 354 FWCI23.63

A review on sentiment analysis from social media platforms

article Full Text OpenAlex 343 FWCI29.8774