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Title: Development of a Fake News Detection Tool for Vietnamese Based on Deep Learning Techniques
Authors: Vo, Trung Hung
Phan, Thi Le Thuyen
Ninh, Khanh Chi
Keywords: fake news detection
natural language processing
Deep Learning
Issue Date: Sep-2022
Publisher: Eastern-European Journal of Enterprise Technologies
Abstract: With the development of the Internet, social networks and different communication channels, people can get information quickly and easily. However, in addition to real and useful news, we also receive false and unreal information. The problem of fake news has become a difficult and unresolved issue. For languages with few users, such as Vietnamese, the research on fake news detection is still very limited and has not received much attention. In this paper, we present research results on building a tool to support fake news detection for Vietnamese. Our idea is to apply text classification techniques to fake news detection. We have built a database of 4 groups of 2 topics about politics (fake news and real news) and about Covid-19 (fake news and real news). Then use deep learning techniques CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to create the corresponding models. When there is new news that needs to be verified, we just need to apply the classification to see which of the four groups they label into to decide whether it is fake news or not. The tool was able to detect fake news quickly and easily with a correct rate of about 85 %. This result will be improved when getting a larger training data set and adjusting the parameters for the machine learning model. These results make an important contribution to the research on detecting fake news for Vietnamese and can be applied to other languages. In the future, besides using classification techniques (based on content analysis), we can combine many other methods such as checking the source, verifying the author's information, checking the distribution process to improve the quality of fake news detection.
Description: Eastern-European Journal of Enterprise Technologies; Vol. 119, Issue 2, pp:14-20.
ISSN: 1729-3774
Appears in Collections:NĂM 2022

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