Comprehensive overview of A Transformer-Based Model for Detecting Fake news and Sentiment Trends on the Social Media Platform
TL;DR
This review paper looks at transformer-based unified models that incorporate sentiment analysis and false tweet detection, and stresses how transformer-based models, such as BERT, RoBERTa, and XLNet, outperform traditional machine learning algorithms due to their attention mechanisms and contextual knowledge.
Abstract
The spread of information, including fake content and deceptive narratives, has been greatly expedited by the quick development of social media platforms, especially Twitter. It is a difficult but critical responsibility to detect such misleading information while also investigating sentiment patterns. This review paper looks at transformer-based unified models that incorporate sentiment analysis and false tweet detection. Transformer architecture creation, usage in social media analytics, datasets, methodologies, assessment criteria, and obstacles are all discussed. The study stresses how transformer-based models, such as BERT, RoBERTa, and XLNet, outperform traditional machine learning algorithms due to their attention mechanisms and contextual knowledge. Finally, future research directions are discussed, including explainable AI and multimodal learning.
