Probability distributions of COVID-19 tweet posted trends uses a nonhomogeneous Poisson process

Devi Munandar, Sudradjat Supian, Subiyanto Subiyanto


The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process with dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no accurence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. Result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic.


Nonhomogeneous Poisson process, NHPP, Tweet, COVID-19, Estimated Parameter, Prediction

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Cifuentes-Amado, M., & Cepeda-Cuervo, E. (2015). Non-Homogeneous Poisson Process to Model Seasonal Events: Application to the Health Diseases. International Journal of Statistics in Medical Research, 4, 337–346.

Franciszek, G. (2018). Nonhomogeneous compound Poisson process application to modeling of random processes related to accidents in the Baltic Sea waters and ports. Journal of Polish Safety and Reliability Association Summer Safety and Reliability Seminars, 9(3).

Grabski, F. (n.d.). Nonhomogeneous Stochastic Processes Connected to Poisson Process. Scientific Journal of Polish Naval Academy, 213(2), 5–15.

Kenney J.F. and Keeping, E. S. (1962). Linear Regression and Correlation. (Ch. 15 in). Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand.

Lee, C., & Wilkinson, D. J. (2020). A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets. Journal of the American Statistical Association, 115(529), 1–15.

Morales, F. E. C., Vicini, L., Hotta, L. K., & Achcar, J. A. (2017). A nonhomogeneous Poisson process geostatistical model. Stochastic Environmental Research and Risk Assessment, 31(2), 493–507.

Ngailo, Triphonia, Shaban, N., Reuder, J., Rutalebwa, E., & Mugume, I. (2016). Non Homogeneous Poisson Process Modelling of Seasonal Extreme Rainfall Events in Tanzania. International Journal of Science and Research (IJSR), 5, 1858–1868.

Ross, S. M. (2014). Introduction to probability models (Eleventh e). Elsevier Inc.

Sumiati, I., Rahmani, U., Supian, S., & Subiyanto, S. (2019). Application of the Nonhomogeneous Poisson Process for Counting Earthquakes.

Vedyushenko, B. A. (2018). Non-homogeneous Poisson process - estimation and simulation. Faculty Of Mathematics and Physics, Charles University, Prague.



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