FakeLens: AI-Powered Fake News Detection

Inspiration

The rapid rise of misinformation and fake news on digital platforms has become a serious global concern. Misleading content spreads quickly, influencing public opinion and creating confusion. This motivated us to build an AI-based system that can automatically detect and classify fake news.

What it does

FakeLens is an AI-powered system that determines whether a news article is real or fake. It analyzes both the title and content using Natural Language Processing (NLP) techniques and classifies the information as reliable or misleading. The goal is to help reduce the spread of misinformation and improve trust in digital content.

How we built it

We began by exploring the provided dataset containing news titles and content. The text data was cleaned through preprocessing steps such as converting to lowercase, removing punctuation, and eliminating stopwords. We then transformed the text into numerical features using TF-IDF vectorization. Finally, we trained machine learning models such as Logistic Regression and Linear SVC, and evaluated their performance using accuracy.

Challenges we ran into

One of the major challenges was handling unstructured and noisy text data. It was also difficult to distinguish between subtly misleading and genuinely real news, as they often share similar linguistic patterns. Additionally, optimizing the model within a limited timeframe while maintaining high accuracy was challenging.

Accomplishments that we're proud of

We successfully developed a working fake news detection system within the hackathon timeframe. The model achieved high accuracy and effectively classified news articles. We also built a clean and efficient NLP pipeline, from preprocessing to final prediction.

What we learned

We gained practical experience in Natural Language Processing, including text preprocessing and feature extraction using TF-IDF. We also learned how to apply machine learning models to real-world problems and understood the importance of data quality and preprocessing in achieving good performance.

What's next for FakeLens: AI-Powered Fake News Detection

We plan to enhance the model using advanced techniques such as BERT and transformer-based architectures. We also aim to develop a real-time web application where users can input news and receive instant predictions. In addition, we plan to extend support for multiple languages and larger, more diverse datasets.