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Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

People are increasingly using social media to consume and share news. The inherent benefits of social media over traditional news media include its low cost and ease of access. In addition, publishing a news article requires less content censorship on social media. The rapid spread of "fake news" on social media, that is, news that contains intentionally false information, has a significant negative impact on society. For instance, false information about the coronavirus disease "2019" has spread around the world like a virus. Therefore, developing effective methods to detect fake news early has great importance. In this paper, the (Efficient Hybrid Features Fake News Detection Methodology) EHF-FNDM model was proposed. It is a classification model for early detection of fake news based on hybrid features. This model was developed to identify fake news based on user profiles, tweets, and replies. It has a user model that can realize whether a user is spreading fake news or not. This user model was essential in determining whether or not the tweet was fake.

Keywords

False information, deep learning, classification model

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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