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@article{180051,
        author = {Maitreyee Jadhav and Mansi Patil and Preshika Giri and Vinay More and Yogita Hande},
        title = {Fake News Detection with Dynamic Model Updates Based on Classifier Comparison},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {592-598},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180051},
        abstract = {In the digital age, the rapid dissemination of 
information through online platforms has led to a 
significant rise in the spread of fake news, posing 
serious threats to societal trust, political stability and 
public safety. This paper focuses on the development 
of a machine learning-based system for dynamic fake 
news detection. The system analyzes textual news 
content to classify it as real or fake using various 
classification algorithms. Initially, data preprocessing 
steps such as tokenization, stopword removal, and 
stemming were applied to clean the dataset. Features 
were extracted using techniques like TF-IDF and 
Count Vectorizer. Multiple models including Logistic 
Regression, Support Vector Machine (SVM), and 
Naive Bayes, were trained and evaluated for 
performance using metrics such as accuracy, precision, 
recall, and F1-score. The model with the highest 
accuracy is selected for deployment. To ensure the 
system remains effective over time, a dynamic model 
updating strategy is implemented, wherein the model 
is periodically retrained with newly labeled data. This 
approach not only enhances prediction accuracy but 
also adapts to evolving patterns in misinformation.},
        keywords = {Fake News Detection, Logistic  Regression, Support Vector Machine, Naïve Bayes},
        month = {May},
        }