Prediction Retweet Using User-Based, Content-Based and Time-Based Features with ANN-Firefly Classification Method

Alif Aqshal; Indri Anugrah Ramadhani

doi:10.55681/armada.v4i5.2014

Authors

Alif Aqshal Faculty of Exact Sciences Education, Universitas Pendidikan Muhammadiyah Sorong, Papua, Indonesia
Indri Anugrah Ramadhani Faculty of Exact Sciences Education, Universitas Pendidikan Muhammadiyah Sorong, Papua, Indonesia

DOI:

https://doi.org/10.55681/armada.v4i5.2014

Keywords:

Retweet Prediction, Artificial Neural Network, Firefly Algorithm, Twitter

Abstract

Retweets on the Twitter platform serve as a critical indicator of information dissemination. However, there is a lack of effective predictive systems to estimate whether a tweet will be retweeted. This research aims to develop a retweet prediction model by leveraging a combination of user-based, content-based, and time-based features. The model is constructed using an Artificial Neural Network (ANN) and optimized through the Firefly Algorithm (FA) to enhance classification accuracy. The dataset was collected from Twitter using a data crawling technique via the Tweepy library, focused on the keyword “Covid Vaccination,” resulting in 12,796 Indonesian-language tweets. The collected data underwent preprocessing stages, including text cleaning, tokenization, normalization, and stemming. The ANN-FA model was then trained to classify the likelihood of a tweet being retweeted. Experimental results demonstrate that the ANN-FA model achieved an accuracy of 90.29%, outperforming the baseline ANN model without optimization. These findings indicate that applying the Firefly Algorithm significantly improves classification performance. The contribution of this study lies in developing a retweet prediction system that integrates multi-dimensional features with metaheuristic optimization, which can be utilized to support digital information dissemination strategies on social media platforms

Downloads

Download data is not yet available.

References

Arifin, E. T., Jondri, & Indwiarti. (2022). Prediksi retweet menggunakan fitur user based dan content based dengan metode klasifikasi ANN-GA.

Alhaidar, Q. J., & Aldrajy, S. M. (2024). Neural network based phishing website detection with using Firefly Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 145–154.

Chao, C. F., & Horng, M. H. (2015). The construction of support vector machine classifier using the Firefly Algorithm. Computational Intelligence and Neuroscience, 2015, 1–10. https://doi.org/10.1155/2015/147640

Febiansyah, M., Jondri, & Indwiarti. (2025). Prediksi retweet berdasarkan konten dan berbasis pengguna dengan metode seleksi classifier.

Firdaus, S. N., Ding, C., & Sadeghian, A. (2018). Retweet: A popular information diffusion mechanism – A survey paper. Online Social Networks and Media, 6, 26–40. https://doi.org/10.1016/j.osnem.2018.04.001

Harahap, J. W., Kurniawan, I., & Nitha, F. (2022). Model klasifikasi berbasis microarray pada identifikasi Parkinson dengan menggunakan metode Firefly Algorithm-Support Vector Machine.

Hoang, T. B. N., & Mothe, J. (2018). Predicting information diffusion on Twitter: Analysis of predictive features. Journal of Computer Science, 28, 257–264. https://doi.org/10.1016/j.jocs.2017.11.002

Khoerunnisa, G., & Astuti, W. (2021). Prediction of retweets based on user, content, and time features using EUSBoost. Journal of Physics: Conference Series, 1845(1), 012028. https://doi.org/10.1088/1742-6596/1845/1/012028

Kumar, V., & Kumar, D. (2020). A systematic review on Firefly Algorithm: Past, present, and future. Archives of Computational Methods in Engineering, 27(2), 547–569. https://doi.org/10.1007/s11831-018-9303-1

Lopez, O. A. M., Lopez, A. M., & Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction. Frontiers in Genetics, 13, 867780. https://doi.org/10.3389/fgene.2022.867780

Mepaiyeda, E. B., & Oluwayumi, I. A. (2023). Prediction of gas hydrate formation temperature in pipelines using artificial neural network (ANN) and Firefly Algorithm (FA). Results in Engineering, 19, 101302. https://doi.org/10.1016/j.rineng.2023.101302

Muhalani, R., Jondri, & Indwiarti. (2025). Prediksi retweet berdasarkan fitur user-based, content-based, dan time-based menggunakan metode ANN-GSO.

Romzi, M. N., & Atastina, I. (2022). Analisis sentiment judul berita ekonomi terhadap indeks harga saham gabungan menggunakan metode Long Short-Term Memory.

Supandi, M. R., Jondri, & Indwiarti. (2023). Retweet prediction using artificial neural network method optimized with Firefly Algorithm. International Journal of Intelligent Engineering and Systems, 16(5), 432–443. https://doi.org/10.22266/ijies2023.1031.38

Syadzily, M. H., Jondri, & Muslim, K. (2024). Prediksi retweet berdasarkan fitur pengguna, konten, dan waktu menggunakan metode klasifikasi ANN-Cat Swarm Optimization.

Zannuar, J. M. S. S., & Lhaksmana, K. M. (2021). Prediksi retweet berdasarkan feature user-based menggunakan metode klasifikasi Random Forest