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


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.

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  1. 1.

    Siegel, D.A.: Social networks and collective action[J]. Am. J. Polit. Sci. 53(1), 122–138 (2009)

    Article  Google Scholar 

  2. 2.

    Wu, L., Ge, Y., Liu, Q., et al.: Modeling the Evolution of Users’ Preferences and Social Links in Social Networking Services[J]. IEEE Trans. Knowl. Data Eng. 29(6), 1240–1253 (2017)

    Article  Google Scholar 

  3. 3.

    Hong, R., He, C., Ge, Y., et al.: User Vitality Ranking and Prediction in Social Networking Services: A Dynamic Network Perspective[J]. IEEE Trans. Knowl. Data Eng. 29(6), 1343–1356 (2017)

    Article  Google Scholar 

  4. 4.

    Chen, Z., Tan, S.M., Campbell, R.H., et al.: Real Time Video and Audio in the World Wide Web[J]. World Wide Web J. (1995)

  5. 5.

    Nugroho, R., Zhao, W., Yang, J., et al.: Using time-sensitive interactions to improve topic derivation in twitter[J]. World Wide Web-internet. Web Inf. Syst. 20(1), 61–87 (2017)

    Article  Google Scholar 

  6. 6.

    Xia, F., Yu, C., Xu, L., et al.: Top- k, temporal keyword search over social media data[J]. World Wide Web-internet. Web Inf. Syst. 20(5), 1–21 (2017)

    Google Scholar 

  7. 7.

    Hu, W., Wang, H., Qiu, Z., et al.: An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent[J]. World Wide Web-internet. Web Inf. Syst. 20(4), 1–21 (2017)

    Google Scholar 

  8. 8.

    Fersini, E., Pozzi, F.A., Messina, E.: Approval network: a novel approach for sentiment analysis in social networks[J]. World Wide Web-internet. Web Inf. Syst. 1–24 (2016)

  9. 9.

    He, W.: Xu G. Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information[J]. Online Inf. Rev. 40(1), 369–370 (2016)

    Article  Google Scholar 

  10. 10.

    Chinchore, A., Jiang, F., Xu, G.: Intelligent Sybil Attack Detection on Abnormal Connectivity Behavior in Mobile Social Networks[C]//International Conference on Knowledge Management in Organizations, pp. 602–617. Springer, Cham (2015)

    Google Scholar 

  11. 11.

    eMarketer. eMarketer Releases Latest Estimates for Worldwide Messaging App Usage[EB/OL]. 21 July 2017

  12. 12.

    Tufekci, Z., Wilson, C.: Social media and the decision to participate in political protest: Observations from Tahrir Square[J]. J. Commun. 62(2), 363–379 (2012)

    Article  Google Scholar 

  13. 13.

    Cihon, P., Yasseri, T.A.: Biased Review of Biases in Twitter Studies on Political Collective Action[J]. Front. Phys. 4(6), 91 (2016)

    Google Scholar 

  14. 14.

    Schneider, N.: Occupy Wall Street[J]. Nation. 29, (2011)

  15. 15.

    Muthiah, S., Huang, B., Arredondo, J., et al.: Planned Protest Modeling in News and Social Media[C]//AAAI, pp. 3920–3927. (2015)

  16. 16.

    Chen, E., Zeng, G., Luo, P., et al.: Discerning individual interests and shared interests for social user profiling[J]. World Wide Web-internet. Web Inf. Syst. 20(2), 417–435 (2017)

    Article  Google Scholar 

  17. 17.

    Xu, G., Li, L., Zhang, Y., et al.: Modeling user hidden navigational behavior for Web recommendation[J]. Web Intelligence Agent Syst. 9(3), 239–255 (2011)

    Google Scholar 

  18. 18.

    Dos Santos, C.N., Gatti, M.: Deep Convolutional Neural Networks for Emotion Analysis of Short Texts[C]//COLING, pp. 69–78. (2014)

  19. 19.

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems, pp. 1097–1105. (2012)

  20. 20.

    Kim, Y.: Convolutional neural networks for sentence classification[C], pp. 1746–1751. arXiv preprint arXiv (2014)

  21. 21.

    Deng, S., Huang, L., Xu, G., et al.: On Deep Learning for Trust-Aware Recommendations in Social Networks.[J]. IEEE Trans. Neural Netw. Learn Syst. 28(5), 1164 (2017)

    Article  Google Scholar 

  22. 22.

    Davydov, A.A., Platov, A.S.: Optimal stationary solution in forest management model by accounting intra-species competition[J]. Moscow Math. J. 12(2), 269–273 (2012)

    MathSciNet  Article  Google Scholar 

  23. 23.

    Eckersten, H., Lundkvist, A., Torssell, B., et al.: Modelling species competition in mixtures of perennial sow-thistle and spring barley based on shoot radiation use efficiency[J]. Acta Agric. Scand. Sect. B Soil Plant Sci. 61(8), 739–746 (2011)

    Google Scholar 

  24. 24.

    Sharma, S., Samanta, G.P.: Optimal harvesting of a two species competition model with imprecise biological parameters[J]. Nonlinear Dyn. 77(4), 1101–1119 (2014)

    MathSciNet  Article  Google Scholar 

  25. 25.

    Foster, K.R., Bell, T.: Competition, not cooperation, dominates interactions among culturable microbial species[J]. Curr. Biol. 22(19), 1845–1850 (2012)

    Article  Google Scholar 

  26. 26.

    Lou, Y., Munther, D.: Dynamics of a three species competition model[J]. Discrete Contin. Dynam. Systems. 32(9), 3099–3131 (2012)

    MathSciNet  Article  Google Scholar 

  27. 27.

    Mirrahimi, S., Perthame, B., Wakano, J.Y.: Evolution of species trait through resource competition[J]. J. Math. Biol. 64(7), 1189–1223 (2012)

    MathSciNet  Article  Google Scholar 

  28. 28.

    Tran, M.V., O’Grady, M., Colborn, J., et al.: Aggression and Food Resource Competition between Sympatric Hermit Crab Species[J]. PLoS One. 9(3), e91823–e91823 (2014)

    Article  Google Scholar 

  29. 29.

    Allesina, S., Levine, J.M.: A competitive network theory of species diversity[J]. Proc. Natl. Acad. Sci. 108(14), 5638–5642 (2011)

    Article  Google Scholar 

  30. 30.

    Torices, R., Méndez, M.: Fruit size decline from the margin to the center of capitula is the result of resource competition and architectural constraints[J]. Oecologia. 164(4), 949–958 (2010)

    Article  Google Scholar 

  31. 31.

    Hou, L., Liu, J., Pan, X., et al.: Prediction of collective opinion in consensus formation[J]. Int. J. Mod. Phys. C. 25(4), 222–237 (2014)

    MathSciNet  Article  Google Scholar 

  32. 32.

    Kallus, N.: Predicting crowd behavior with big public data[C]// Pro-ceedings of the companion publication of the 23rd international con-ference on World wide web companion, pp. 625–630. International World Wide Web Conferences Steering Committee (2014)

  33. 33.

    Bland, J.M., Altman, D.G.: Statistics notes: measurement error[J]. BMJ. 313(7059), 744 (1996)

    Article  Google Scholar 

  34. 34.

    Ma, L., Juefei-Xu, F., Sun, J., et al.: DeepGauge: Comprehensive and Multi-Granularity Testing Criteria for Gauging the Robustness of Deep Learning Systems[J]. arXiv preprint arXiv:1803.07519 (2018)

  35. 35.

    Lu, P.: Predicting peak of participants in collective action[J]. Appl. Math. Comput. 274, 318–330 (2016)

    MathSciNet  MATH  Google Scholar 

  36. 36.

    Ranganath, S., Morstatter, F., Hu, X., et al.: Predicting Online Protest Participation of Social Media Users[C]//AAAI, pp. 208–214. (2016)

  37. 37.

    González-Bailón, S., Borge-Holthoefer, J., Rivero, A., et al.: The dynamics of protest recruitment through an online network[J]. Sci. Rep. 1, 197 (2011)

    Article  Google Scholar 

  38. 38.

    Khandpur, R.P., Ji, T., Ning, Y., et al.: Determining Relative Airport Threats from News and Social Media[C]//AAAI, pp. 4701–4707. (2017)

  39. 39.

    Zhao, J., Dong, L., Wu, J., et al.: MoodLens: an emoticon-based sentiment analysis system for Chinese tweets[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531. ACM (2012)

  40. 40.

    Scheve, C.V., Ismer, S.: Towards a Theory of Collective Emotions[J]. Emot. Rev. 5(4), 406–413 (2013)

    Article  Google Scholar 

  41. 41.

    Xiong, X.B., Zhou, G., Huang, Y.Z., et al.: Dynamic evolution of collective emotions in social networks: a case study of Sina weibo[J]. SCIENCE CHINA Inf. Sci. 56(7), 1–18 (2013)

    MathSciNet  Article  Google Scholar 

  42. 42.

    Ferrara, E., Yang, Z.: Measuring Emotional Contagion in Social Media.[J]. PLoS One. 10(11), (2014)

    Article  Google Scholar 

  43. 43.

    Kramer, A.D.I., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks[J]. Proc. Natl. Acad. Sci. 111(24), 8788–8790 (2014)

    Article  Google Scholar 

  44. 44.

    Vincent, P., Larochelle, H., Lajoie, I., et al.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. J. Mach. Learn. Res. 11(12), 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  45. 45.

    LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition[J]. Proc. IEEE. 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  46. 46.

    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning[J]. Nature. 521(7553), 436–444 (2015)

    Article  Google Scholar 

  47. 47.

    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks[J]. Science. 313(5786), 504–507 (2006)

    MathSciNet  Article  Google Scholar 

  48. 48.

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems, pp. 1097–1105. (2012)

  49. 49.

    Sun, Y., Wang, X., Tang, X.: Deep Learning Face Representation from Predicting 10,000 Classes[C]// IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE Computer Society (2014)

  50. 50.

    Silver, D., Huang, A., Maddison, C.J., et al.: Mastering the game of Go with deep neural networks and tree search[J]. Nature. 529(7587), 484 (2016)

    Google Scholar 

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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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Yang, W., Liu, X., Liu, J. et al. Prediction of collective actions using deep neural network and species competition model on social media. World Wide Web 22, 2379–2405 (2019).

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  • Collective action
  • Emotional prediction
  • Automatic label
  • Deep neural network
  • Early premonitions