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A Method for Measuring Popularity of Popular Events in Social Networks Using Swarm Intelligence

  • Jiaying ChenEmail author
  • Zhenyu Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

Social network is a platform for users to post and forward contents that they are paying attention to. So through the measurement of the popularity of the event, it is possible to excavate social focus and predict the development trend of the event. The user is used as the main body to measure the popularity, so as to construct the interaction graph, and the indicator of the graph has a degree distribution sequence, a clustering coefficient and a degree centrality. Among them, the user distribution of the degree distribution sequence of the interaction graph shows the distribution of “power law distribution”, and the power index introduced by the degree distribution sequence can effectively reflect the distribution of the user’s participation degree in the popular event; the clustering coefficient reflects the user’s agglomeration in the popular event; the degree centrality reflects the dominant position of the user participating in the popular event; the number of users reflects the size of the network formed by the popular event. The comprehensive indicator obtained after nondimensionalization and analytic hierarchy process of these four indicators can comprehensively and accurately measure the popularity of popular events. The comprehensive indicator shows that the popular event of the “Ching Ming Festival” is more consistent with the actual situation during the day. Further more, the measure of popularity is more sensitive and can reflect minor changes in the popular spot.

Keywords

Interaction graph Event popularity Analytic hierarchy process 

Notes

Acknowledgement

This work is supported by Natural Science Foundation of China (No. 61502246), NUPTSF (No. NY215019).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institution of Internet of ThingsNanjing University of Posts and TelecommunicationsNanjingChina

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