No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection

TL;DR

This paper presents a multilingual approach for COVID-19 fake tweet detection using BERT models, creating annotated datasets for Hindi and Bengali, and achieving high accuracy with zero-shot learning.

Contribution

It introduces a novel multi-Indic-language fake news detection framework with annotated datasets and zero-shot learning, surpassing existing methods in low-resource languages.

Findings

Achieved around 89% F-Score in fake tweet detection.

Established the first benchmark for Hindi and Bengali fake news detection.

Zero-shot approach reaches about 81% F-Score without annotated data.

Abstract

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and the spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine-tuned over created dataset in Hindi and Bengali. We also propose a zero-shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero-shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach.