Fake News Detection Data Annotation & NLP Training Project
Worked on a fake news detection project focused on building high-quality labeled datasets for training natural language processing models. The project involved annotating large volumes of news articles, headlines, and social media content by classifying them into categories such as “Fake,” and “Real.” Applied consistent labeling guidelines to ensure accuracy and reduce bias, while handling ambiguous or context-dependent cases through careful analysis. Performed data cleaning, validation, and quality checks to improve dataset reliability. Contributed to refining annotation standards and ensuring inter-annotator consistency. The dataset supported machine learning models designed for misinformation detection, enhancing their ability to identify deceptive or low-credibility content. Maintained high attention to detail and met strict quality benchmarks throughout the project.