Abstract
It is widely understood that diffusion of and simultaneous interactions between narratives—defined here as persistent point-of-view messaging—significantly contributes to the shaping of political discourse and public opinion. In this work, we propose a methodology based on Multi-Variate Hawkes Processes and our newly-introduced Process Influence Measures for quantifying and assessing how such narratives influence (Granger-cause) each other. Such an approach may aid social scientists enhance their understanding of socio-geopolitical phenomena as they manifest themselves and evolve in the realm of social media. In order to show its merits, we apply our methodology on Twitter narratives during the 2019 Venezuelan presidential crisis. Our analysis indicates a nuanced, evolving influence structure between 8 distinct narratives, part of which could be explained by landmark historical events.
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Notes
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Narrative and stance labelling was carried out by the data provider and was provided to us as is with very limited description of the process followed.
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In particular, we chose two-day time frames to reduce the computational burden of training. Also, we used an hourly timescale to represent event time stamps to maintain the numerical stability of our training algorithm.
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References
Alvari, H., Shakarian, P.: Hawkes process for understanding the influence of pathogenic social media accounts. In: 2019 2nd International Conference on Data Intelligence and Security (ICDIS), pp. 36–42 (2019). https://doi.org/10.1109/ICDIS.2019.00013
Blackburn, M., et al.: Corpus development for studying online disinformation campaign: a narrative + stance approach. In: STOC@LREC (2020)
Blackburn, M., Yu, N., Memory, A., Mueller, W.G.: Detecting and Annotating Narratives in Social Media: A Vision Paper. ICWSM, US, June 2020. https://doi.org/10.36190/2020.23
Calderaro, A.: Social media and politics. In: Outhwaite, W., Turner, S. (eds.) The SAGE Handbook of Political Sociology, 1st edn., 2v, chap. 44, pp. 781–796. Sage Publications Ltd., December 2017
Cryst, E., Ponce de León, E., Suárez Pérez, D., Perkins, S.: Bolivarian factions: Facebook takes down inauthentic assets. Technical report, Stanford’s Freeman Spogli Institute for International Studies, September 2020
Eichler, M., Dahlhaus, R., Dueck, J.: Graphical modeling for multivariate hawkes processes with nonparametric link functions. J. Time Ser. Anal. 38(2), 225–242 (2017). https://doi.org/10.1111/jtsa.12213
Farajtabar, M., Gomez-Rodriguez, M., Wang, Y., Li, S., Zha, H., Song, L.: COEVOLVE: a joint point process model for information diffusion and network co-evolution. In: Companion Proceedings of the The Web Conference 2018, WWW 2018, pp. 473–477. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3184558.3186236
Hawkes, A.G.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1), 83–90 (1971). https://doi.org/10.2307/2334319
Hindman, M.: The Myth of Digital Democracy. Princeton University Press, Princeton (2009)
Jost, J.T., et al.: How social media facilitates political protest: information, motivation, and social networks. Polit. Psychol. 39(S1), 85–118 (2018). https://doi.org/10.1111/pops.12478
Jungherr, A.: Twitter use in election campaigns: a systematic literature review. J. Inf. Technol. Polit. 13(1), 72–91 (2016). https://doi.org/10.1080/19331681.2015.1132401
Lai, E., et al.: Topic time series analysis of microblogs. IMA J. Appl. Math. 81, hxw025 (2016). https://doi.org/10.1093/imamat/hxw025
Margetts, H., John, P., Hale, S., Yasseri, T.: Political Turbulence: How Social Media Shape Collective Action. Princeton University Press, Princeton (2015)
Mohler, G., McGrath, E., Buntain, C., LaFree, G.: Hawkes binomial topic model with applications to coupled conflict-Twitter data. Ann. Appl. Stat. 14(4), 1984–2002 (2020). https://doi.org/10.1214/20-AOAS1352
Pires, T., Schlinger, E., Garrette, D.: How multilingual is multilingual BERT? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4996–5001. Association for Computational Linguistics, Florence, July 2019. https://doi.org/10.18653/v1/P19-1493
Sytnik, A.: Digitalization of diplomacy in global politics on the example of 2019 Venezuelan presidential crisis. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O., Musabirov, I. (eds.) DTGS 2019. CCIS, vol. 1038, pp. 187–196. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37858-5_15
Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 123–131. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2339530.2339552
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003, pp. 267–273. Association for Computing Machinery, New York (2003). https://doi.org/10.1145/860435.860485
Zhang, W., Johnson, T.J., Seltzer, T., Bichard, S.L.: The revolution will be networked: the influence of social networking sites on political attitudes and behavior. Social Sci. Comput. Rev. 28(1), 75–92 (2010). https://doi.org/10.1177/0894439309335162
Zhou, K., Zha, H., Song, L.: Learning social infectivity in sparse low-rank networks using multi-dimensional Hawkes processes. In: Carvalho, C.M., Ravikumar, P. (eds.) Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 31, pp. 641–649. PMLR, Scottsdale, 29 April–01 May 2013. http://proceedings.mlr.press/v31/zhou13a.html
Acknowledgments
This work was supported by the U.S. Defense Advanced Research Projects Agency (DARPA) Grant No. FA8650-18-C-7823 under the Computational Simulation of Online Social Behavior (SocialSim) program of DARPA’s Information Innovation Office. Any opinions, findings, conclusions, or recommendations contained herein are those of the authors and do not necessarily represent the official policies or endorsements, either expressed or implied, of DARPA, or the U.S. Government. Finally, the authors would like to thank the manuscript’s anonymous reviewers for their helpful comments and suggestions.
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Aravamudan, A., Zhang, X., Song, J., Fiore, S.M., Anagnostopoulos, G.C. (2021). Influence Dynamics Among Narratives. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_20
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