DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING
DOI:
https://doi.org/10.33480/jitk.v11i3.7472Keywords:
Machine Learning, Micro-Virality, Smotenc, Social Listening, TikTok Content AnalyticsAbstract
The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
Downloads
References
[1] D. Chalipah et al., “The Essence of TikTok Social Media Content: Opportunities and Challenges in Popularizing Local Cultural Identity,” 2024. [Online]. Available: https://journal.cerdasnusantara.org/index.php/harmoni
[2] Dr. R. Jeswani, “The Role and Importance of Social Media Marketing in Brand Building,” Irish Interdisciplinary Journal of Science & Research, vol. 07, no. 04, pp. 01–09, 2023, doi: 10.46759/iijsr.2023.7401.
[3] A. Krowinska and D. Dineva, “The role and forms of social media branded content driving active customer engagement behaviours,” Journal of Marketing Management, 2025, doi: 10.1080/0267257X.2025.2544808.
[4] A. Keir et al., “Building a community of practice through social media using the hashtag #neoEBM,” PLoS One, vol. 16, no. 5 May 2021, May 2021, doi: 10.1371/journal.pone.0252472.
[5] R. Rajapaksha, S. Silva, S. Labs, and S. Lanka, “Predictive Analysis on Social Media Content to Become Viral,” 2023. [Online]. Available: https://www.researchgate.net/publication/381852269
[6] I. Mediansyah, F. Septian, and A. Zikry, “Penerapan Whale Optimization Algorithm dalam Pengoptimalan Portofolio Investasi Menggunakan Model Prediktif Artificial Intelligence,” Journal of Software Engineering and Computational Intelligence, vol. 2, no. 1, 2024.
[7] P. Warakmulty, D. Yeffry, and H. Putra, “Optimalisasi Manajemen Sentimen di Media Sosial Universitas melalui Machine Learning dan AI: Studi Kasus pada Komentar Instagram Optimizing Sentiment Management on University Social Media through Machine learning and AI: A Case Study on Instagram Comments,” Jurnal Tata Kelola dan Kerangka Kerja, vol. 11, no. 1, pp. 31–38, 2025.
[8] Q. Chang, P. Wu, S. Wang, and M. Zhang, “Exploring educational hypogamy among women in urban and rural China: Insights from random forest machine learning,” PLoS One, vol. 20, no. 9 September, Sep. 2025, doi: 10.1371/journal.pone.0331744.
[9] Rahmanul Hoque, Suman Das, Mahmudul Hoque, and Mahmudul Hoque, “Breast Cancer Classification using XGBoost,” World Journal of Advanced Research and Reviews, vol. 21, no. 2, pp. 1985–1994, Feb. 2024, doi: 10.30574/wjarr.2024.21.2.0625.
[10] T. Ahmed, “Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry,” Journal of Electrical Systems and Information Technology, vol. 12, no. 1, Jun. 2025, doi: 10.1186/s43067-025-00221-0.
[11] I. A. H. Hidayah, R. Kusumawati, Z. Abidin, and M. Imamuddin, “Analysis of Public Sentiment Towards The TikTok Application Using The Naive Bayes Algorithm and Support Vector Machine,” Journal of Computer Networks, Architecture and High Performance Computing, vol. 6, no. 2, pp. 881–891, Jun. 2024, doi: 10.47709/cnahpc.v6i2.3990.
[12] R. Erama, “Pemanfaatan Platform Cloud Google Colab Untuk Scraping Komentar Tiktok Pada Konten Gorontalo sebagai Dasar Analisis Respons Warganet,” Journal of Applied Engineering Science, vol. 1, no. 2, pp. 124–134, Dec. 2025, doi: 10.65177/jaes.v1i2.38.
[13] R. Siddik, Roswaty, and Meilin Veronica, “Pengaruh Konten Kreatif, Interaksi Pengguna dan Popularitas Influencer Terhadap Keputusan Pembelian Konsumen Pada Program Afiliasi TikTok,” JEMSI (Jurnal Ekonomi, Manajemen, dan Akuntansi), vol. 10, no. 2, pp. 1048–1058, Apr. 2024, doi: 10.35870/jemsi.v10i2.2251.
[14] R. Dewi, R. Sri Hayati, A. Saleh, D. Yani, H. Tanjung, and ; Abwabul Jinan, “Enhancing Machine Learning Algorithm Performance for PCOS Diagnosis Using SMOTENC on Imbalanced Data,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 11, no. 1, 2025, doi: 10.33480/jitk.v11i1.6676.
[15] M. Farid Naufal, A. Fernando Susanto, C. Nathaneil Kansil, S. Huda, and K. kunci, “Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Hilangnya Nasabah Bank Application of Machine Learning to Predict Potential Loss of Bank Customer,” Feb. 2023.
[16] J. Zhang et al., “Weak Preprocessing Iris Feature Matching Based on Bipartite Graph,” IET Signal Processing, vol. 2025, no. 1, 2025, doi: 10.1049/sil2/2013549.
[17] T. Gori, A. Sunyoto, and H. Al Fatta, “Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 215–224, Feb. 2024, doi: 10.25126/jtiik.20241118074.
[18] I. Hilal Ramadhan, R. Priatama, A. Akalili, and F. Kulau, “Analisis Teknik Digital Marketing pada Aplikasi Tiktok (Studi Kasus Akun TikTok @jogjafoodhunterofficial),” online) Socia: Jurnal Ilmu-ilmu Sosial, vol. 18, no. 1, pp. 49–60, 2021.
[19] J. Elektronika and D. Komputer, “Mengoptimalkan Proses Pembersihan Data dalam Analisis Big Data Menggunakan Pipeline Berbasis AI,” Jurnal Elektronika dan Komputer, vol. 17, no. 2, 2024, doi: 10.51903/elkom.v17i2.2311.
[20] V. R. Danestiara, M. Marwondo, and N. N. Azkiya, “Prediction of Inhibitor Binding Affinity and Molecular Interactions in Mpro Dengue Using Machine Learning,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 10, no. 3, pp. 461–468, Feb. 2025, doi: 10.33480/jitk.v10i3.5994.
[21] M. Z. Lv, K. L. Li, J. Z. Cai, J. Mao, J. J. Gao, and H. Xu, “Evaluation of landslide susceptibility based on SMOTE-Tomek sampling and machine learning algorithm,” PLoS One, vol. 20, no. 5 May, May 2025, doi: 10.1371/journal.pone.0323487.
[22] J. Hu and S. Szymczak, “A review on longitudinal data analysis with random forest,” Mar. 01, 2023, Oxford University Press. doi: 10.1093/bib/bbad002.
[23] Y. Nie and Y. Xu, “Prediction On Tiktok Like Behavior Based on Random Forest Model,” 2024.
[24] D. N. Handayani and S. Qutub, “Penerapan Random Forest Untuk Prediksi Dan Analisis Kemiskinan,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 2, pp. 405–412, May 2025, doi: 10.31004/riggs.v4i2.512.
[25] P. Zhang, Y. Jia, and Y. Shang, “Research and application of XGBoost in imbalanced data,” Int. J. Distrib. Sens. Netw., vol. 18, no. 6, Jun. 2022, doi: 10.1177/15501329221106935.
[26] H. Al Aziz and H. A. Santoso, “Model Prediksi Stunting Anak di Indonesia Menggunakan Extreme Gradient Boosting,” Jurnal Algoritma, 2025, doi: 10.33364/algoritma/v.22-1.2289.
[27] D. Firdaus, I. Sumardi, and C. Chazar, “Deteksi Serangan Pada Jaringan Internet Of Things Medis Menggunakan Machine Learning Dengan Algoritma XGBoost,” 2025.
[28] A. F. Zain, H. Al Azies, and K. Ananda, “Analisis Sentimen Ulasan Pengguna iPhone dengan Pendekatan Hibrida RoBERTa dan XGBoost,” Jurnal Algoritma, May 2025, doi: 10.33364/algoritma/v.22-1.2277.
[29] J. Gu and E. Lee, “Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS),” Applied Sciences (Switzerland), vol. 15, no. 14, Jul. 2025, doi: 10.3390/app15147722.
[30] M. Jamhuri and T. Utomo, “Penggunaan Particle Swarm Optimization pada Jaringan Syaraf Tiruan untuk Klasifikasi Sinyal Radar,” 2024. [Online]. Available: https://archive.ics.uci.edu/dataset/52/ionosphere.
[31] A. B. Kurniati, W. A. Sidik, and Jajang, “Model Artificial Neural Networks (ANN) untuk Prediksi COVID-19 di Indonesia,” JST (Jurnal Sains dan Teknologi), vol. 12, no. 3, Jan. 2024, doi: 10.23887/jstundiksha.v12i3.53437.
[32] J. Choi, “Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback,” Applied Sciences (Switzerland), vol. 15, no. 9, May 2025, doi: 10.3390/app15095198.
[33] M. Illahi, “Ensemble Machine Learning Approach for Stress Detection in Social Media Texts,” Quaid-e-Awam University Research Journal of Engineering, Science & Technology, vol. 20, no. 2, pp. 123–128, Dec. 2022, doi: 10.52584/qrj.2002.15.
[34] Shahriar Siddique, Hossain Muhammad Ebrahim, and Miah Md Saef Ullah, “Explainable AI in Feature Selection: Improving Classification Performance on Imbalanced Datasets,” 2024.
[35] R. Field, A. Garland, H. Link, W. Pease, E. Roll, and S. Verzi, “Social Media Analytics Relevant to TikTok-a Literature Review and Directions for Future Research,” 2024. [Online]. Available: https://classic.ntis.gov/help/order-methods/
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ratih Anggraeni, Purwadi, Pungkas Subarkah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
