Factorization of Fact-Checks for Low Resource Indian Languages
This addresses the problem of fake news proliferation in India for non-English speakers, providing a foundational dataset for research, though it is incremental as it extends existing fact-checking efforts to new languages.
The authors tackled the lack of fact-checking resources for low-resource Indian languages by introducing FactDRIL, a large-scale multilingual dataset covering 11 languages with 22,435 total samples, including 9,058 in English, 5,155 in Hindi, and 8,222 across regional languages.
The advancement in technology and accessibility of internet to each individual is revolutionizing the real time information. The liberty to express your thoughts without passing through any credibility check is leading to dissemination of fake content in the ecosystem. It can have disastrous effects on both individuals and society as a whole. The amplification of fake news is becoming rampant in India too. Debunked information often gets republished with a replacement description, claiming it to depict some different incidence. To curb such fabricated stories, it is necessary to investigate such deduplicates and false claims made in public. The majority of studies on automatic fact-checking and fake news detection is restricted to English only. But for a country like India where only 10% of the literate population speak English, role of regional languages in spreading falsity cannot be undermined. In this paper, we introduce FactDRIL: the first large scale multilingual Fact-checking Dataset for Regional Indian Languages. We collect an exhaustive dataset across 7 months covering 11 low-resource languages. Our propose dataset consists of 9,058 samples belonging to English, 5,155 samples to Hindi and remaining 8,222 samples are distributed across various regional languages, i.e. Bangla, Marathi, Malayalam, Telugu, Tamil, Oriya, Assamese, Punjabi, Urdu, Sinhala and Burmese. We also present the detailed characterization of three M's (multi-lingual, multi-media, multi-domain) in the FactDRIL accompanied with the complete list of other varied attributes making it a unique dataset to study. Lastly, we present some potential use cases of the dataset. We expect this dataset will be a valuable resource and serve as a starting point to fight proliferation of fake news in low resource languages.