NLP and Deep Learning Methods for Curbing the Spread of Misinformation in India. Issue 3 (2nd September 2021)

• Record Type: Journal Article
• Authors: Nigam, Amber; Jaiswal, Pragati; Sundar, Saketh; Poddar, Mukund; Kumar, Nitya; Dernoncourt, Franck; Celi, Leo A.
• Is Part Of: International journal of intelligence, security, and public affairs. Volume 23:Issue 3(2021)
• Page Start: 216
• Page End: 227
• Publication Date: 2021-09-02
• Subjects: Infodemic -- deep learning -- impact category -- misinformation -- policy making; Security, International -- Periodicals; Intelligence service -- Periodicals; Business intelligence -- Periodicals; 327.05
• DOI: 10.1080/23800992.2021.1975429
• Languages: English
• ISSNs: 2380-0992

Abstract

ABSTRACT: The current fight against COVID-19 is not only around its prevention and cure but it is also about mitigating the negative impact resulting from misinformation around it. The pervasiveness of social media and access to smartphones has propelled the spread of misinformation on such a large scale that it is considered as one of the main threats to our society by the World Economic Forum. This 'Infodemic' has caused widespread rumors, fueled practices that can jeopardize one's health, and has even resulted in hate violence in certain parts of the world. We built an engine that has the ability to match incoming text, which may contain correct or incorrect information, with a known repository of misinformation. By matching texts on embeddings generated using BERT, we evaluated paraphrased texts to see if they matched texts previously labeled as misinformation. Further, we augmented an existing data corpus of texts by tagging each misinformation with one or more impact categories. We may be able to take specific actions to avert the consequence of misinformation if we can predict the particular ramification of a certain type of misinformation.