Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
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.
A multi-model learning framework that combines the complementary strengths of classical machine learning classifiers, deep sequential neural networks, and transformer-based contextual language models to detect fake news on social media is proposed.
Priya Verma· International Journal of Res...· 0 citations
A robust and scalable framework for real-time rumor detection powered by Large Language Models, like BERT, RoBERTa, and GPTs-4, which combines Natural Language Processing techniques with sentiment analysis, stance detection, and automated fact-checking to enhance contextual understanding and assess credibility more effectively.
Priyanshi Borase, S. Kolhe· ITEGAM- Journal of Engineeri...· 0 citations
This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing techniques and multiple machine learning algorithms to automatically classify news articles as real or fake.
Shahid Khan, Abdul Majid Farooqi· International Scientific Jou...· 0 citations
The growth of social media platforms results in billions of user-generated messages daily, making automated text analysis critical. The global impact of disinformation, the evolution of cyber threats toward psychological tactics, and the growing political and commercial value of public opinion have made sentiment analysis an important and relevant area of research. Although existing reviews have examined sentiment analysis approaches in terms of methods and application areas, few have evaluated these approaches in the context of cyber threat detection. This paper examines sentiment analysis methods applied to social media text data, covering lexicon-based, machine
learning, and deep learning approaches, including transformerbased architectures, as well as widely used datasets. The paper also discusses how sentiment analysis can be applied to the detection of
threats that exploit human emotions, including phishing, disinformation, and social engineering. To support this, an empirical analysis of large language model performance is conducted, measuring their ability to detect emotionally manipulative content. The purpose of this paper is to provide
readers with an objective understanding of sentiment analysis and its role as a defense against socially engineered cyber threats.
Vusal Shahbazov· “Kibertəhlükəsizlik və rəqəm...· 0 citations
A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.
M. E. Babu, G. Sukanya· International Journal for Re...· 0 citations
Fake news is spreading quickly on the internet, which is very bad for society and the security of the government. The significant issue that was talked about in the paper was the creation of automatic systems that can detect fake news better and adapt to various areas. The dataset used in the study is the LIAR dataset, which is a standard set of various political statements labeled with varying degrees of truthfulness. Text is also cleaned up, tokenized, and represented with existing trained word embeddings such as GloVe and Word2Vec as a step in data preparation. To identify complex trends in the text, most language and contextual features are removed, such as syntactic, semantic, and sentiment-based ones. The primary contribution of this study is a way of grouping various features into one representation. A set of models is subjected to performance tests, and it includes Random Forest, Naive Bayes, Convolutional Neural Network (CNN), Autoencoder, and a proposed Hybrid CNN-Autoencoder architecture. The hybrid model performs the most, having the greatest precision and the most equalized classification scores. Comparative analysis demonstrates that the combination of deep learning and knowledge of the environment significantly enhances the level of detection in domains. It is a flexible AI-based system that can work in the context of language and political differences and is a big step forward in searching for fake information automatically.
Pundlik Dattatray Jadhav, R. K. Shukla· international journal of eng...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.