Deep Learning Based Fake News Detection

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Kimaya Urane
Arati Deshpande


Social network connectivity is one of the most important countermeasures in today's world. we must use with caution or risk creating disaster problems and causing social upheaval. To address this problem, items and stories that spread quickly must be tracked for a set period of time. In this proposed method, we attempted to determine whether the news being disseminated around the world is genuine or not. Factors responsible for fake news detection are also discussed. So that disinformation can be controlled and has a direct impact on society's citizens. Analytical and advanced deep learning techniques are combined with natural language processing techniques. We gathered information from open sources, such as Kaggle. Following that, we used NLP techniques to preprocess and analyze the data. In the form of exploratory data analysis, there is a detailed representation of graphical plots (EDA). To gain a better understanding of the data through more precise statistics.

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How to Cite
Urane, K., and A. . Deshpande. “Deep Learning Based Fake News Detection”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 7, July 2022, pp. 94-99, doi:10.17762/ijritcc.v10i7.5578.
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