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International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation

SBP-BRiMS 2021: Social, Cultural, and Behavioral Modeling pp 204–213Cite as

Influence Dynamics Among Narratives

A Case Study of the Venezuelan Presidential Crisis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12720))

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

  1. 1.

    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.

  2. 2.

    https://www.reuters.com/article/us-venezuela-politics-un/venezuelans-facing-unprecedented-challenges-many-need-aid-internal-u-n-report-idUSKCN1R92AG.

  3. 3.

    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.

  4. 4.

    https://www.bbc.com/news/world-latin-america-47036129.

  5. 5.

    https://www.nytimes.com/2019/01/13/world/americas/venezeula-juan-guaido-arrest.html.

  6. 6.

    https://www.nytimes.com/2019/01/21/world/americas/venezuela-maduro-national-guard.html.

  7. 7.

    https://www.theguardian.com/world/2019/jan/23/venezuela-protests-thousands-march-against-maduro-as-opposition-sees-chance-for-change.

  8. 8.

    https://about.fb.com/news/2020/09/august-2020-cib-report/.

  9. 9.

    https://blog.twitter.com/en_us/topics/company/2019/further_research_information_operations.html.

References

  1. 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

  2. Blackburn, M., et al.: Corpus development for studying online disinformation campaign: a narrative + stance approach. In: STOC@LREC (2020)

    Google Scholar 

  3. 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

  4. 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

    Google Scholar 

  5. 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

    Google Scholar 

  6. 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

  7. 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

  8. 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

  9. Hindman, M.: The Myth of Digital Democracy. Princeton University Press, Princeton (2009)

    Google Scholar 

  10. 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

  11. 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

  12. Lai, E., et al.: Topic time series analysis of microblogs. IMA J. Appl. Math. 81, hxw025 (2016). https://doi.org/10.1093/imamat/hxw025

  13. Margetts, H., John, P., Hale, S., Yasseri, T.: Political Turbulence: How Social Media Shape Collective Action. Princeton University Press, Princeton (2015)

    Google Scholar 

  14. 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

  15. 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

  16. 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

    Chapter  Google Scholar 

  17. 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

  18. 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

  19. 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

  20. 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

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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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Authors and Affiliations

  1. Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA

    Akshay Aravamudan, Xi Zhang & Georgios C. Anagnostopoulos

  2. University of Central Florida, Orlando, FL, USA

    Jihye Song & Stephen M. Fiore

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  1. Akshay Aravamudan
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  2. Xi Zhang
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  3. Jihye Song
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  4. Stephen M. Fiore
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  5. Georgios C. Anagnostopoulos
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Correspondence to Akshay Aravamudan .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, NY, USA

    Robert Thomson

  2. University of Arkansas at Little Rock, Little Rock, AR, USA

    Muhammad Nihal Hussain

  3. Bucknell University, Lewisburg, PA, USA

    Christopher Dancy

  4. United States Military Academy, West Point, NY, USA

    Aryn Pyke

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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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  • DOI: https://doi.org/10.1007/978-3-030-80387-2_20

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