AI Strategy and Concepts Bibliography

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Aggarwal, A., Chauhan, A., Kumar, D., Mittal, M., & Verma, S. (2020). Classification of fake news by fine-tuning deep bidirectional transformers based language model. EAI Endorsed Transactions on Scalable Information Systems Online First; EAI: Ghent, Belgium, 
Added by: SijanLibrarian (2020-10-28 13:51:57)   Last edited by: SijanLibrarian (2020-10-28 13:54:29)
Resource type: Journal Article
BibTeX citation key: Aggarwal2020
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Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General, Mathematics, Military Science
Subcategories: Behavioral analytics, Cognitive Electronic Warfare, Cross-domain deterrence, Cyber, Deep learning, Forecasting, Game theory, JADC2, Machine learning, Machine recognition, Markov models, Q-learning, Social cognition
Creators: Aggarwal, Chauhan, Kumar, Mittal, Verma
Collection: EAI Endorsed Transactions on Scalable Information Systems Online First; EAI: Ghent, Belgium
Views: 46/67
Views index: 18%
Popularity index: 4.5%

With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by approximately eight percent.

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