Big Data Technologies in Political Processes: Risks and Opportunities

The article deals with various aspects related to the use of Big Data technologies in political processes. Digital technologies have an ambivalent impact on the social and political processes, creating the “grey zone” of opportunities and resources that are the subject of conflicts and competition among various political agents. This statement is equally true concerning election campaigns. Firstly, the author describes the concept of data-driven campaign, which is rapidly spreading due to the demand for flexible management mechanisms and the formation of the “attention economy”. The implementation of the concept includes processes of data mining and analysis, microtargeting — the article reveals the content of each stage on the example of recent cases. The essential advantage of using big data analysis in political processes is concluded not only in the scale of the data mining but also in the possibility to examine deep causal relationships and dependencies, which extends the range of opportunities to influence political agents behaviour. Secondly, it is possible to extrapolate mechanisms of data-driven campaign to the level of data-driven politics. The author formulates the major risks and threats associated with the use of Big Data in political processes: funnel of mistrust in political institutions and technologies, blurring political institutions and plebiscite democracy, the preservation and confidentiality of personal data, the consequences of algorithms cognitive restrictions. As a result, in the short term it will be relevant to provide institutional regulation of data using, as well as to support the development of human capital as the basic skills of personal data protection and the use of modern technologies.

big data, data mining and data analysis, psychometrics, data-driven campaign, microtargeting, digital footprint, election campaign, Cambridge Analytica, cybersecurity, funnel of mistrust

1. Scott J. Two Cheers for Anarchism: Six Easy Pieces on Autonomy, Dignity and Meaningful Work and Play. Princeton University Press; 2012.

2. Rifkin J. The Zero Marginal Cost Society: The Internet of Things, the collaborative commons, and the eclipse of capitalism. Palgrave Macmillan; 2014.

3. Thaler R., Sunstein C. Nudge: Improving Decisions about Health, Wealth and Happiness. Yale University Press; 2008.

4. Kollmuss A., Agyeman J. Mind the Gap: Why do people act environmentally and what are the barriers to proenvironmental behavior? Environmental Education Research. 2002;8(3):239–260.

5. Lanham R. The Economics of Attention. Style and Substance in the Age of Information. Chicago: Chicago University Press; 2006.

6. Venturini T., Rogers R. “API-Based Research” or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica Data Breach. Digital Journalism. 2019;7(40):532–540.

7. Bruns A. After the “APIcalypse”: social media platforms and their fight against critical scholarly research. Information Communication and Society. 2019;22(2):1–23.

8. Jan F., Valikoski T-R., Grau J. Listening in the Business Context: Reviewing the State of Research. The International Journal of Listening. 2008;22(2):141–151.

9. Deulkar D., Gupta P. A Study on Usage of Online Personal Information by Data Brokers. International Research Journal of Engineering and Technology. 2018;5(5):3522–3527.

10. Golbeck J., Robles C., Edmondson M., Turner K. Predicting personality from Twitter. IEEE International Conference on Social Computing; 2011.

11. Quercia D., Lambiotte R., Stillwell D., Kosinski M., Crowcroft J. The personality of popular Facebook users. Proceedings of the ACM 2012 conference on computer supported cooperative work; 2012.

12. Segalin C., Perina A., Cristani M., Vinciarelli A. The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits. IEEE Trans Affect Comput. 2017;8(2):268–285.

13. Kramer A. D., Guillory J. E., Hancock J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Science of the USA. 2014;111(24):8788–8790.

14. Fording R., Schram S. The Cognitive and Emotional Sources of Trump Support: The Case of Low-Information Voters. New Political Science. 2017;39(4):670–686.

15. Mukhametov D. R. Problems and prospects of realisation of the concept “smart city” in Russia (on the example of Moscow). World of the new economy. 2019;13(3):81–88. (In Russ.).

16. Erokhina O. V. “Smart Moscow”: a new concept of development of the capital city. Gumanitarnye nauki. Vestnik Finansovogo universiteta. 2018;8(3):6–10. (In Russ.).

17. Pentland A. Social Physics: How Good Ideas Spread — The Lessons from a New Science. New York: The Penguin Press; 2014.

18. Rocher L., Hendrickx J., de Montjoye Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications. 2019;10(1):145–171.

19. O’Neil С. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. N.Y.: Crown Books; 2016.

20. Pasquale F. The black box society: The secret algorithms that control money and information. Cambridge: Harvard University Press; 2015.

21. Greenfield А. Radical Technologies: The Design of Everyday Life. Moscow: Izdatel’skij dom Delo; 2018. (In Russ.).