9 papers
Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention
Jingyuan Yi, Peiyang Yu, Tianyi Huang +1
This work proposes an LSTM-based sentiment classification model with multi-head attention mechanism and TF-IDF optimization. Through the integration of TF-IDF feature extraction an…
Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies
Tianyi Huang, Jingyuan Yi, Peiyang Yu +1
The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLa…
A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
Tianyi Huang, Zeqiu Xu, Peiyang Yu +2
In this paper, we propose an optimized Transformer model that integrates Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and apply it to fake news classifica…
Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media
Jiani Wang, Xiaochuan Xu, Peiyang Yu +1
Detecting and classifying cyberbullying in social media is hard because of the complex nature of online language and the changing nature of content. This study presents a multi-sta…
Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions
Jingyuan Yi, Zeqiu Xu, Tianyi Huang +1
The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic insti…
A Hybrid Attention Framework for Fake News Detection with Large Language Models
Xiaochuan Xu, Peiyang Yu, Zeqiu Xu +1
With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Lan…