Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic
Abstract
Technology and information growth make all internet users can play a role in disseminating information, including hoax news. One way that can be done to avoid hoax news is to look for sources of information, but valid news is not always perceived as 'true' by individuals because human judgments can lead to bias. Several studies on automatic hoax news classification have been carried out using various deep learning approaches such as the pre-trained multilingual transformer model. This study focuses on classifying Indonesian hoax news using the pre-trained transformer multilingual model (XLM-R and mBERT) combined with a BERTopic model as a topic distribution model. The result shows that the proposed method outperforms the baseline model in classifying fake news in the low-resource language (Indonesian) with accuracy, precision, recall, and F1 results of 0.9051, 0.9515, 0.8233, and 0.8828 respectively.
DOI:
https://doi.org/10.31449/inf.v46i8.4336Downloads
Published
11/16/2022
How to Cite
Hutama, L. B., & Suhartono, D. (2022). Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic. Informatica, 46(8). https://doi.org/10.31449/inf.v46i8.4336
Issue
Section
Online-only
License
Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.
All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.
Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.
