COMBINED DATASET
The dataset used in this study comprises 82,946 labeled news statements collected from multiple publicly available fact-checking and news verification sources focusing on Indian and Indic language content. Each instance is annotated for binary classification, wit
The dataset used in this study comprises 82,946 labeled news statements collected from multiple publicly available fact-checking and news verification sources focusing on Indian and Indic language content. Each instance is annotated for binary classification, with labels Real (0) and Fake (1). The dataset exhibits a natural class imbalance, consisting of 53,713 Fake samples (64.76%) and 29,233 Real samples (35.24%).
The corpus spans a diverse set of languages, including English, Hindi, Tamil, Gujarati, Malayalam, Punjabi, Bengali, Telugu, Marathi, Nepali, and other low-resource languages. It also contains romanized and code-mixed text, reflecting realistic social media usage patterns in multilingual Indian settings. Language identifiers were retained to support language-wise evaluation.
Data from different sources were merged into a unified format, retaining only semantically meaningful fields: news text, label, and language.
The dataset’s scale, linguistic diversity, and presence of code-mixing make it suitable for evaluating multilingual transformer models for Indic fake news detection.
📤 Share this page
Found this useful? Share it with your network.
Files are hosted on the source repository. Click download to access the full dataset.