Big data prediction of durations for online collective actions based on peak’s timing

Shizhao Nie, Zheng Wang, Wangmo Pujia, Yuan Nie and Peng Lu

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 138-154

Abstract: Peak Model states that each collective action has a life circle, which contains four periods of “prepare”, “outbreak”, “peak”, and “vanish”; and the peak determines the max energy and the whole process. The peak model’s re-simulation indicates that there seems to be a stable ratio between the peak’s timing (TP) and the total span (T) or duration of collective actions, which needs further validations through empirical data of collective actions. Therefore, the daily big data of online collective actions is applied to validate the model; and the key is to check the ratio between peak’s timing and the total span. The big data is obtained from online data recording & mining of websites. It is verified by the empirical big data that there is a stable ratio between TP and T; furthermore, it seems to be normally distributed. This rule holds for both the general cases and the sub-types of collective actions. Given the distribution of the ratio, estimated probability density function can be obtained, and therefore the span can be predicted via the peak’s timing. Under the scenario of big data, the instant span (how long the collective action lasts or when it ends) will be monitored and predicted in real-time. With denser data (Big Data), the estimation of the ratio’s distribution gets more robust, and the prediction of collective actions’ spans or durations will be more accurate.

Keywords: Big data; Prediction; Peak’s timing; Ratio; Span; Collective actions (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:138-154

DOI: 10.1016/j.physa.2017.09.059

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