A machine learning application for raising WASH awareness in the times of COVID-19 pandemic

• Published in: Scientific reports Vol. 12; no. 1; p. 810
• Main Authors: Pandey, Rohan, Gautam, Vaibhav, Pal, Ridam, Bandhey, Harsh, Dhingra, Lovedeep Singh, Misra, Vihaan, Sharma, Himanshu, Jain, Chirag, Bhagat, Kanav, Arushi, Patel, Lajjaben, Agarwal, Mudit, Agrawal, Samprati, Jalan, Rishabh, Wadhwa, Akshat, Garg, Ayush, Agrawal, Yashwin, Rana, Bhavika, Kumaraguru, Ponnurangam, Sethi, Tavpritesh
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 692/700/478; Artificial intelligence; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disinformation; False information; Female; Global Health; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Male; multidisciplinary; Natural Language Processing; Pandemics; Public health; Science; Science (multidisciplinary); Segmentation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-03869-6

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.