Fake News Detection System is a crucial tool in combating the spread of misinformation, especially in today's digital age where false information can quickly proliferate across various platforms. This project aims to address this issue by employing Machine Learning techniques to predict the authenticity of news articles. By leveraging datasets from the 2016 U.S. Presidential Elections obtained from Kaggle, the project focuses on training models to differentiate between fake and real news articles based on their textual content.
Fake news poses a significant societal problem, influencing public perception and potentially impacting crucial events such as elections. The rise of fake news, particularly during the 2016 U.S. Presidential Election, underscores the urgent need for effective detection mechanisms. Traditional news outlets and social media platforms struggle to combat this issue, emphasizing the importance of automated systems for identifying and flagging fake news articles.
Several studies and research efforts have explored various methodologies for fake news detection. From leveraging machine learning algorithms to analyzing social media data, these approaches aim to develop robust systems capable of discerning between genuine and false information sources.
The Fake News Detection System involves preprocessing and cleaning the data, applying feature engineering techniques, and employing machine learning algorithms for classification. Key components include understanding the characteristics of fake news, selecting appropriate machine learning algorithms, and evaluating their accuracy.
The methodology comprises essential steps such as data collection, preprocessing, analyzing the data, selecting and training machine learning models, and testing and predicting outcomes. Techniques such as Count Vectorizer and TF-IDF Vectorizer are utilized for feature engineering, transforming textual data into numerical representations suitable for machine learning algorithms.
The performance of different machine learning models is evaluated based on their accuracy in predicting fake news. Models such as Logistic Regression, Naïve Bayes, and Passive Aggressive Classifier are assessed, with Passive Aggressive Classifier demonstrating the highest accuracy among the tested models.
The project concludes that Passive Aggressive Classifier is well-suited for predicting fake news using the provided dataset. Future work may involve further refinement of the models, exploration of advanced machine learning techniques, and integration with real-time news monitoring systems to enhance accuracy and scalability.
The project draws upon existing research and studies in the field of fake news detection, leveraging insights and methodologies from various scholarly articles and conference papers.
For more information and detailed implementation, please refer to the full documentation and source code provided in this repository.
Contributors: Abdul Hadi, Mohd Nabeed Uddin, Syed Hashmath Pasha, Rahmat Ali
Affiliations:
- B.E students, Department of Computer Science and Engineering, ISL Engineering College, Affiliated to Osmania University, Hyderabad, India
- Faculty of Computer Science and Engineering, ISL Engineering College, Affiliated to Osmania University, Hyderabad, India