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Found 13 papers, 1 papers with code
Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States
Disgust was the predominant emotion associated with misinformation tweets in the US, while anticipation was the predominant emotion in India.
Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases
Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs). Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0. 80 and 0. 62 respectively.
Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring
The current empirical study was undertaken to formulate a concrete automated data quality platform to assess the quality of incoming dataset and generate a quality label, score and comprehensive report.
WiseR: An end-to-end structure learning and deployment framework for causal graphical models
Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data.
The State of Infodemic on Twitter
Following the wave of misinterpreted, manipulated and malicious information growing on the Internet, the misinformation surrounding COVID-19 has become a paramount issue.
Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings
We also observed a significant change in the linear trends of categories like hesitation and contentment before and after approval of vaccines.
Learning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in India
A bootstrapped, ensemble-averaged Bayesian Network structure was learned to quantify the factors that could maximize condom usage as revealed from the model.
A Cross-lingual Natural Language Processing Framework for Infodemic Management
In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization.
(Un)Masked COVID-19 Trends from Social Media
Wearing masks is a useful protection method against COVID-19, which has caused widespread economic and social impact worldwide.
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine.
Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic
In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India.
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
Conclusion: We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation.
Learning to Address Health Inequality in the United States with a Bayesian Decision Network
In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features.
