Fake News Detection

In today's digital age, the spread of misinformation poses a serious threat to public discourse and trust in media. For my university thesis, I tackled this challenge by developing a text-based fake news detection system powered by deep learning.

Unlike many approaches that rely on metadata or social media patterns, my work focuses solely on the textual content of news articles—proving that language alone can reveal a lot.

Key components of the system:

LSTM: for understanding sequential context

CNN: for identifying local linguistic patterns

BERT: for capturing deep contextual meaning through attention mechanisms

? Each model was trained and tested on diverse datasets (Fake News Corpus & TI-CNN), allowing robust performance across domains.

The outcome? A modular, scalable framework capable of detecting fake news without auxiliary signals—paving the way for future research in trustworthy AI and media integrity.