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.
The rapid proliferation of user-generated content on social media platforms has transformed the way information is created, shared, and consumed. While this democratization of information has clear benefits, it has also enabled the large-scale dissemination of fake news, which poses serious threats to public health, political stability, financial markets, and social cohesion. Automatic fake news detection has therefore become an active and urgent research problem in computer science. This paper proposes 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. The framework integrates lexical, semantic, and contextual features through a weighted soft-voting ensemble that fuses predictions from a Support Vector Machine, a Bidirectional Long Short-Term Memory network, and a fine-tuned BERT encoder. Experiments were conducted on three widely used public benchmark datasets, namely LIAR, FakeNewsNet, and ISOT, comprising more than one hundred thousand labeled news statements and articles. The proposed ensemble achieved an accuracy of 96.4% and an F1-score of 96.1% on the ISOT dataset, outperforming each individual constituent model and several competitive baselines reported in the literature. Ablation studies confirm that the transformer component contributes the largest performance gain, while the ensemble fusion improves robustness and reduces variance across datasets. The results demonstrate that combining heterogeneous learning paradigms yields a more accurate and generalizable fake news detector than any single model in isolation. The paper also discusses computational cost, interpretability, and limitations, and outlines directions for multimodal and cross-lingual extensions.
Mrs Priyal Verma· International Journal of Res...· 0 citations
The rapid increase in the content generated by users on the social media has significantly accelerated the dissemination of rumors and misinformation, creating serious societal challenges. This paper introduces a robust and scalable framework for real-time rumor detection powered by Large Language Models, like BERT, RoBERTa, and GPTs-4. The proposed system combines Natural Language Processing techniques with sentiment analysis, stance detection, and automated fact-checking to enhance contextual understanding and assess credibility more effectively. Data is collected from various social media platforms like Twitter, Facebook, and Reddit, along with benchmark datasets like PHEME and FakeNewsNet. Experimental results show that LLMs significantly outperform traditional machine learning models, with GPT-4 achieving an accuracy of up to 94.7%. An ablation study further highlights the contribution of each module to the overall performance of GPT -4 for Rumor Detection. This framework offers a very comprehensive and flexible approach for countering misinformation across a range of languages and social environments.
Priyanshi Borase, S. Kolhe· ITEGAM- Journal of Engineeri...· 0 citations
ABSTRACT
The rapid proliferation of fake news and misinformation across digital platforms poses a serious threat to democratic processes, public health, and social stability. This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing (NLP) techniques and multiple machine learning algorithms. The proposed system analyzes textual content, linguistic patterns, writing style, and sentiment cues to automatically classify news articles as real or fake.
Several classification algorithms — including Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Long Short-Term Memory (LSTM) neural networks — are implemented and compared. Feature extraction is performed using TF-IDF and Bag of Words representations. The system is evaluated using standard metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.
Experimental results demonstrate that deep learning models, particularly LSTM, achieve superior contextual understanding, while traditional models offer faster inference. The paper also discusses current challenges such as sarcasm detection, multilingual content, and computational constraints, along with future directions including real-time monitoring and transformer-based architectures. This work contributes significantly to the broader effort of combating misinformation through technological innovation and provides a comprehensive roadmap for future research in this critical area.
Keywords: Fake News Detection, Natural Language Processing, Machine Learning, Deep Learning, LSTM, TF-IDF, Text Classification, Misinformation, Social Media
Shahid Khan, Dr. 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