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Prediction of collective actions using deep neural network and species competition model on social media

  • Wei Yang
  • Xiao Liu
  • Jin Liu
  • Xiaohui CuiEmail author
Article
  • 65 Downloads

Abstract

Collective actions that can affect government management and public security (e.g., mass demonstrations), usually undergo long term development and originate from small and uncertain social media activities. Thus, researchers try to identify a collective action from various aspects such as changes in communication patterns, emerging keywords, and social emotions. Many studies aim to predict whether regular social media activities can evolve into collective actions, but the accuracy of these predictions is far from desirable. To address such a problem, we propose a framework named PFDNN which can predict the occurrence probability of collective actions every single day in the next month, so as to provide a reference for early decision-making. The framework consists of two parts: collective emotional contagion prediction and deep neural network with fully-connected layers (DNN) prediction. First, we implement the emotional contagion prediction based on species competition model to forecast user’s emotional state. Second, we model the DNN prediction as a binary classification problem that can be implemented using a DNN discriminator based on emotional contagion prediction. The DNN discriminator considers early premonitions based on the number of tweets, the embedded emotions and the number of violence-related words in the tweets during a specific timeframe, and automatically labels the early premonitions according to the number of reports published in the mainstream media. For evaluation purpose, we analyze the topics related to the “Arab Spring” from over 300,000 social media entries using TensorFlow. The results demonstrate that our prediction framework performs better than other representative methods.

Keywords

Collective action Emotional prediction Automatic label Deep neural network Early premonitions 

Notes

Acknowledgements

The authors would like to acknowledge the support provided by the National Key R&D Program of China (No.2018YFC1604000), the Fundamental Research Funds for the Central Universities of China (2042017gf0035), the grands of the National Natural Science Foundation of China (61572374, U163620068, U1135005, 61572371), Open Fund of Key Laboratory of Network Assessment Technology from CAS, Guangxi Key Laboratory of Trusted Software (No.kx201607), the Academic Team Building Plan for Young Scholars from Wuhan University (WHU2016012) and the Natural science foundation of Hubei province (No.2017CFB663).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Cyber Science and EngineeringWuhan UniversityWuhanChina
  2. 2.School of Information TechnologyDeakin UniversityMelbourneAustralia
  3. 3.State Key Laboratory of Software Engineering, Computer SchoolWuhan UniversityWuhanChina
  4. 4.Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnoloyGuilinChina
  5. 5.Key Laboratory of Network Assessment TechnologyInstitute Of Information Engineering, Chinese Academy Of SciencesBeijingChina

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