Hoax Detection Tweets of the COVID-19 on Twitter Using LSTM-CNN with Word2Vec
DOI:
https://doi.org/10.30865/mib.v6i4.4564Keywords:
Hoax, Twitter, LSTM, CNN, Word2VecAbstract
The growth of Twitter users is increasing every year, impacting activities in social media such as hoaxes that are increasingly widespread on various platforms. During this pandemic, the rate of hoaxes is growing because nowadays, it is very easy for humans to interact with each other, have opinions, and exchange information. One of the hoaxes that often appears is the hoax about the Covid-19 virus. Therefore, a method for detecting hoaxes is needed, especially for the topic of the Covid-19 virus in Indonesia. The method used in hoax detection is LSTM-CNN with Word2Vec. More than 1000 tweets data are used in this study, divided into hoax and non-hoax categories. Detection is carried out to analyze the hoax results obtained by using Word2Vec as a method to convert data as a classification vector and LSTM-CNN to classify the data. This work's result showed that the LSTM-CNN model with Word2Vec achieves 79.71% accuracy, surpassing the LSTM model and CNN model.References
L. Rizkinaswara, “Kominfo Temukan 1.819 Isu Hoaks Seputar Covid-19,†Kominfo. https://aptika.kominfo.go.id/2021/08/kominfo-temukan-1-819-isu-hoaks-seputar-covid-19/ (accessed Oct. 26, 2021).
K. Azizah, “Hoax adalah Berita Bohong, Kenali Ciri-Ciri, Jenis, dan Cara Mengatasinya,†Merdeka. https://www.merdeka.com/trending/hoax-adalah-berita-bohong-kenali-ciri-ciri-jenis-dan-cara-mengatasinya-kln (accessed Oct. 26, 2021).
C. Olah, “Understanding LSTM Networks,†Colah.github.io. http://colah.github.io/posts/2015-08-Understanding-LSTMs (accessed Dec. 5, 2021).
I. Y. R. Pratiwi, R. A. Asmara, and F. Rahutomo, “Study of hoax news detection using naïve bayes classifier in Indonesian language,†in 2017 11th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, Oct. 2017, pp. 73–78. doi: 10.1109/ICTS.2017.8265649.
B. P. Nayoga, R. Adipradana, R. Suryadi, and D. Suhartono, “Hoax Analyzer for Indonesian News Using Deep Learning Models,†Procedia Comput. Sci., vol. 179, pp. 704–712, 2021, doi: 10.1016/j.procs.2021.01.059.
P. Reddy, D. Roy, P. Manoj, M. Keerthana, and P. Tijare, “A Study on Fake News Detection Using Naïve Bayes, SVM,†Neural Netw. LSTM J Adv Res Dyn Control Syst, vol. 1, pp. 942–947, 2019.
H. Mustofa and A. A. Mahfudh, “Klasifikasi Berita Hoax Dengan Menggunakan Metode Naive Bayes,†Walisongo J. Inf. Technol., vol. 1, no. 1, pp. 1–12, 2019.
F. N. Rozi and D. H. Sulistyawati, “KLASIFIKASI BERITA HOAX PILPRES MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBOR DAN PEMBOBOTAN MENGGUNAKAN TF-IDF,†KONVERGENSI, vol. 15, no. 1, Oct. 2019, doi: 10.30996/konv.v15i1.2828.
P. M. Sosa, “Twitter sentiment analysis using combined LSTM-CNN models,†Eprint Arxiv, pp. 1–9, 2017.
A. K. Cotra, “Analysis On Tweets Using Python and TWINT,†Towards Data Science. Analysis On Tweets Using Python and TWINT (accessed Jun. 26, 2022).
W. Kurniasih, “Pengertian Hoaks: Sejarah, Jenis, Contoh, Penyebab dan Cara Menghindarinya. [Online] Gramedia,†Gramedia. https://www.gramedia.com/literasi/pengertian-hoaks/ (accessed Nov. 09, 2021).
Z. Li, “A Beginner’s Guide to Word Embedding with Gensim Word2Vec Model,†Towards Data Science. https://towardsdatascience.com/a-beginners-guide-to-word-embedding-with-gensim-word2vec-model-5970fa56cc92 (accessed Jun. 27, 2022).
W. Widayat, “Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning,†J. MEDIA Inform. BUDIDARMA, vol. 5, no. 3, p. 1018, Jul. 2021, doi: 10.30865/mib.v5i3.3111.
D. Karani, “Introduction to word embedding and word2vec,†Data Sci., vol. 1, 2018.
B. Jang, I. Kim, and J. W. Kim, “Word2vec convolutional neural networks for classification of news articles and tweets,†PloS One, vol. 14, no. 8, p. e0220976, 2019.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,†Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
M. Rajdev and K. Lee, “Fake and Spam Messages: Detecting Misinformation During Natural Disasters on Social Media,†in 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, Dec. 2015, pp. 17–20. doi: 10.1109/WI-IAT.2015.102.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
