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OriginalPaper | Open access | Published: July 31, 2023

Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method

Fauzaan Rakan Tama, Yuliant Sibaroni
JINAV: Journal of Information and Visualization, Vol. 4 No. 1 (2023), pp. 70-78 https://doi.org/10.35877/454RI.jinav1525 Published: 2023-07-31
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Abstract

The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.

Keywords

social media, hoax, Twitter, convolutional neural network, tf-idf, tf-rf

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How to Cite

Tama, F. R., & Sibaroni, Y. . (2023). Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method . JINAV: Journal of Information and Visualization, 4(1), 70–78. https://doi.org/10.35877/454RI.jinav1525