Detecting Fake News on Social Media Combined with the CNN Methods

Abstract

Social media platforms are created to facilitate human social life as technology develops. Twitter is one of the most popular and frequently used social media for exchanging information. This social media platform disseminates real-time and complete information. Unfortunately, there are not a few tweets that contain false information or are often referred to as hoaxes. Those hoaxes that existed on Twitter are very troubling for society. Fake news or hoaxes can cause misunderstandings in receiving information. Therefore, this research aimed at developing a system that can detect hoaxes on Twitter to anticipate their spread, which can be detrimental to related parties. The system being developed uses a deep learning approach with the Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and Global Vectors (GloVe). The results of this study display the fake news detected by the system using the CNN method with baseline, BERT, and GloVe. The data have been adjusted to the keywords related to fake news and spread on online media, such as Hoax or Not from Detik.com, CekFakta from Kompas.com, etc. The results show the highest accuracy of 98.57% using CNN with a split ratio of 90:10, baseline unigram-bigram, BERT, and Top10 corpus tweet+IndoNews with an increase of 4.7%.

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