JINAV: Journal of Information and Visualization
Vol. 4 No. 1 (2023)

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

Fauzaan Rakan Tama (Telkom Unversity)
Yuliant Sibaroni (Telkom Unversity)

Article Info

Publish Date
02 Feb 2023

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%.

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Journal Info
JINAV: Journal of Information and Visualization
Website

Abbrev

jinav

Publisher

PT. Mattawang Mediatama Solution

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics

Description

JINAV: Journal of Information and Visualization is an international peer-reviewed open-access journal dedicated to interchange for the results of high-quality research in all aspects of information science and technology, data, knowledge, communication, and their visualization. The journal publishes ...