YouTube Viewership Increation Analysis and Prediction using Facebook Prophet Model

 (*)Rezqie Hardi Pratama Mail (Telkom University, Bandung, Indonesia)
 Putu Harry Gunawan (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: December 25, 2023; Published: January 24, 2024


YouTube, a widely accessed video-sharing platform available through both mobile applications and web interfaces, serves as a medium for content creators, commonly referred to as YouTubers, to engage with their audience. The success of a YouTuber is intricately tied to their audience engagement, encompassing metrics such as total views, comments, and likes garnered by their videos. This study involves the analysis of 7,600 English-language videos uploaded on YouTube between August and September 2020. To assess the predictive success value of a video, the study employs the Facebook Prophet method. Focusing on the upload time as a primary parameter, this method forecasts the growth in the number of YouTube viewers using datasets obtained from the YouTube API. Leveraging Time Series modeling, Facebook Prophet processes data by considering audience interactions throughout a video broadcast. The results derived from the Facebook Prophet model indicate a predictive trend of increasing viewership on YouTube in the coming months. The evaluation of model linearity, measured using the R² score to gauge data reliability, reveals a score of 0.39 or 39% which indicates a positive linearity score. And using Pearson correlation it gives 75 accuracy score. This signifies the model's capability to reasonably predict the growth in the number of viewers, contributing valuable insights into the dynamics of YouTube audience engagement over time.


Time Series; Youtube; Facebook Prophet; Prediction; R² Score; Pearson

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J. Arthurs, S. Drakopoulou, and A. Gandini, “Researching YouTube,” Convergence, vol. 24, no. 1, pp. 3–15, 2018, doi: 10.1177/1354856517737222.

D. Indrawan, S. R. Cakrawijaya, B. D. Wicaksono, E. Erni, and W. Gata, “Prediksi Jumlah Penonton Video Youtube Menggunakan Model Deep Neural Network (Dnn),” J. Inf. Syst. Informatics Comput., vol. 5, no. 1, p. 94, 2021, doi: 10.52362/jisicom.v5i1.463.

K. Y. Hern, L. K. Yin, and C. W. Yoke, “Forecasting Facebook User Engagement Using Hybrid Prophet and Long Short-Term Memory Model.” International Conference on Digital Transformation and Applications (ICDXA), Penang, Malaysia, 2021.

M. S. Irshad, A. Anand, and M. Agarwal, “Modeling Active Life Span of YouTube Videos Based on Changing Viewership-Rate.” Revista Investigation Operacional, 2020.

S. Yang, D. Brossard, D. A. Scheufele, and M. A. Xenos, “The science of YouTube: What factors influence user engagement with online science videos?,” PLoS One, vol. 17, no. 5 May, pp. 1–19, 2022, doi: 10.1371/journal.pone.0267697.

K. Yousaf and T. Nawaz, “A Deep Learning-Based Approach for Inappropriate Content Detection and Classification of YouTube Videos,” IEEE. 2022.

E. Žunić, K. Korjenić, K. Hodžić, and D. Đonko, “Application of Facebook’s Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data,” Int. J. Comput. Sci. Inf. Technol., vol. 12, no. 2, pp. 23–36, 2020, doi: 10.5121/ijcsit.2020.12203.

C. Materials, M. Khayyat, and K. Laabidi, “Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction,” no. March, 2021, doi: 10.32604/cmc.2021.014918.

F. T. B. Sitepu, V. A. P. Sirait, and R. Yunis, “Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Prophet Facebook.” Paradigma, 2021. doi: 10.31294/p.v23il.9756.

M. M. Hossain, N. Garg, A. H. . F. Anwar, M. Prakash, and M. Bari, “Monthly Rainfall Prediction for Decadal Timescale using Facebook Prophet at Catchment Level.” researchgate, 2021.

C. B. Aditya Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, “Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 524–532, 2021, doi: 10.1016/j.procs.2021.01.036.

M. A. Haq, “CDLSTM: A Novel Model for Climate Change Forecasting,” Tech Sci. Press, 2021.

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not.” Geoscientific Model Development, 2022. doi:

H. N. Yashinta, A. Aklis, and F. B. Sari, “Akurasi Analisis Time Series Dengan Metode Rmse Pada Forecasting Harga Saham Bank Syariah Yang Ada Di BEI,” Al-Muhtarifin Islam. Bank. …, vol. 1, no. 1, pp. 65–73, 2022, [Online]. Available:

P. Calista, P. Yones, and S. Muthaiyah, “Asia Paci fi c Management Review eWOM via the TikTok application and its in fl uence on the purchase intention of somethinc products,” Asia Pacific Manag. Rev., vol. 28, no. 2, pp. 174–184, 2023, doi: 10.1016/j.apmrv.2022.07.007.

F. Rustam et al., “COVID-19 Future Forecasting Using Supervised Machine Learning Models,” IEEE Access, vol. 8, pp. 101489–101499, 2020, doi: 10.1109/ACCESS.2020.2997311.

E. Van Den Heuvel and Z. Zhan, “Myths About Linear and Monotonic Associations : Pearson ’ s r , Spearman ’ s ρ , and Kendall ’ s τ,” Am. Stat., vol. 0, no. 0, pp. 1–19, 2022, doi: 10.1080/00031305.2021.2004922.

Pawan and R. Dhiman, “Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface,” Meas. Sensors, vol. 25, no. November 2022, p. 100616, 2023, doi: 10.1016/j.measen.2022.100616.

M. M. Muzakki and F. Nhita, “The spreading prediction of Dengue Hemorrhagic Fever (DHF) in Bandung regency using K-means clustering and support vector machine algorithm,” 2018 6th Int. Conf. Inf. Commun. Technol. ICoICT 2018, vol. 0, no. c, pp. 453–458, 2018, doi: 10.1109/ICoICT.2018.8528782.

A. Alsharef, K. Aggarwal, Sonia, M. Kumar, and A. Mishra, “Review of ML and AutoML Solutions to Forecast Time-Series Data,” Arch. Comput. Methods Eng., vol. 29, no. 7, pp. 5297–5311, Nov. 2022, doi: 10.1007/s11831-022-09765-0.

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