Fake News Detection

The fake news detection dataset used in this project contains labeled news articles categorized as either "fake" or "real." These articles have been collected from credible real-world sources and fact-checking websites, ensuring diverse and high-quality data. The dataset includes textual features such as the news content, along with metadata like publication date, author, and source details. On average, articles vary in length, providing a rich linguistic variety for model training. The dataset is balanced to minimize bias between fake and real news categories, supporting robust classification. It often contains thousands to hundreds of thousands of articles, enabling effective machine learning model development and evaluation. Additionally, some versions of the dataset may also include image URLs for multimodal analysis, expanding the detection capability beyond text alone. This comprehensive dataset plays a critical role in training and validating the fake news detection model used in this project.

Here is a description for each column header of the fake news dataset:

id: A unique identifier assigned to each news article in the dataset for easy reference and indexing.

headline: The title or headline of the news article, summarizing the key news story in brief.

written by: The author or journalist who wrote the news article; this may sometimes be missing or anonymized.

news: The full text content of the news article, which is the main body used for analysis and classification.

label: The classification label indicating the authenticity of the news article, typically a binary value such as "fake" or "real" (or 0 for real and 1 for fake), indicating whether the news is deceptive or truthful.

This detailed column description provides clarity on the structure and contents of the dataset used for fake news detection modeling.