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Amrita Bhattacharjee
Researcher at Arizona State University
Publications - 9
Citations - 96
Amrita Bhattacharjee is an academic researcher from Arizona State University. The author has contributed to research in topics: Computer science & Disinformation. The author has an hindex of 3, co-authored 5 publications receiving 30 citations.
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Journal ArticleDOI
Combating disinformation in a social media age
Kai Shu,Amrita Bhattacharjee,Faisal Alatawi,Tahora H. Nazer,Kaize Ding,Mansooreh Karami,Huan Liu +6 more
TL;DR: An overview of the techniques explored to date for the combating of disinformation with various forms is presented, including different forms of disinformation, and factors related to the spread of disinformation are discussed.
Proceedings ArticleDOI
Text Transformations in Contrastive Self-Supervised Learning: A Review
TL;DR: The contrastive learning framework is formalized, the considerations that need to be addressed in the data transformation step are emphasized, and the state-of-the-art methods and evaluations for contrastive representation learning in NLP are reviewed.
Posted Content
Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics
TL;DR: This position paper of combating the COVID-19 infodemic is illustrated by providing real-world examples of rampant conspiracy theories, misinformation, and various types of scams that take advantage of human kindness, fear, and ignorance.
Proceedings Article
Challenges in combating COVID-19 infodemic - Data, tools, and ethics
TL;DR: In this article, the authors present three key challenges in this fight against the COVID-19 infodemic where researchers and practitioners instinctively want to contribute and help and demonstrate that these challenges can and will be effectively addressed by collective wisdom, crowd sourcing, and collaborative research.
Journal ArticleDOI
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach
TL;DR: This article used reinforcement learning methods to solve the Wordle puzzle, which applied to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) problems.