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@article{180059,
        author = {Vaishali Shirsath and Soham Pashte and Akash Keni and Shreyas Pathe},
        title = {TruthCheck: Real-Time Fake News Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {330-335},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180059},
        abstract = {Fake news spreads rapidly online, affecting 
public trust and media credibility. This paper presents 
TruthCheck, a real-time fake news detection system 
optimized for web and mobile deployment. The 
proposed approach utilizes a Convolutional Neural 
Network (CNN) and Naïve Bayes to classify news 
articles, leveraging TensorFlow Lite for efficient 
execution. We compare three algorithms—CNN, Naïve 
Bayes, and Logistic Regression—to determine the most 
suitable model for mobile applications. While Logistic 
Regression and Naïve Bayes are computationally 
efficient, their performance is limited in capturing 
complex patterns. CNN, in contrast, processes text as 
spatial data, offering higher accuracy with deeper 
contextual understanding. Our model is trained on a 
Twitter-based Kaggle dataset, achieving high accuracy 
with minimal computational overhead. Experimental 
results demonstrate CNN’s effectiveness in real-time 
misinformation detection, making TruthCheck a 
practical tool for verifying news content and 
combating fake news efficiently.},
        keywords = {Fake News Detection, Deep Learning,  Convolutional Neural Network (CNN), Naïve Bayes,  Natural Language Processing (NLP), TensorFlow Lite,  Mobile  AI,  Real-Time  News  Classification,  Misinformation Detection, Text Classification, Machine  Learning},
        month = {May},
        }