Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media


  • Dion Pratama Putra * Mail Telkom University, Bandung, Indonesia
  • Erwin Budi Setiawan Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Hoax; Twitter; Long Short Term Memory (LSTM); Gate Recurent Unit (GRU); GloVe

Abstract

There are negative effects of the ease of obtaining information in today's society, one of which is the rise of hoaxes on the internet. As much as 92.40% of social media platforms such as Twitter are used to spread hoaxes. Launched on July 13, 2006, Twitter is a microblogging service where users can spread information at no cost to themselves or others. In this study, the authors will conduct hoax news detection on Twitter social media using the Long Short - Term Memory (LSTM) method and Gate Recurent Unit (GRU) and gloVe feature expansion. with a dataset of 25,234 data which produces accuracy results in TF-IDF on each model, namely 97.33% in LSTM and 96.75% in GRU, and an increase in accuracy of 0.22% in the tweet corpus on LSTM and an increase in accuracy of 0.15 in the BeritaTweet corpus on GRU.

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Author Biography

Erwin Budi Setiawan, Telkom University, Bandung

School of Computing, Informatics

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Article History
Submitted: 2023-02-03
Published: 2023-03-30
Abstract View: 464 times
PDF Download: 439 times
How to Cite
Putra, D., & Setiawan, E. (2023). Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media. Building of Informatics, Technology and Science (BITS), 4(4), 1815−1820. https://doi.org/10.47065/bits.v4i4.3084
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