- Код статьи
- S013216250017615-9-
- DOI
- 10.31857/S013216250017615-9
- Тип публикации
- Статья
- Статус публикации
- Опубликовано
- Авторы
- Том/ Выпуск
- Том / Номер 11
- Страницы
- 219-226
- Аннотация
With the growing methodological possibilities of research in sociology using digital data, there is a need for theoretical models corresponding to digital research tools. The article shows the construction of a possible theoretical optics of sociology in order to use the analytical potential of digital methods and data to the fullest extent possible. An attempt is made to outline the contours of a theoretical model corresponding to digital research tools. Based on the thesis that theories depend on the methodological tools of the researcher, the idea of making digital footprints a standalone subject of social research is developed. The concept of replications proposed by D. Boullier, the French sociologist, and traced back to the sociology of G. Tarde is considered as a promising theoretical framework for conceptualizing digital footprints. The theoretical optics of digital footprints as replications is interpreted as a basis for rethinking the problem of micro- and macro-level connections in sociology.
- Ключевые слова
- digital data, digital footprints, sociological theory, structure, replications, actor network theory, G. Tarde, B. Latour
- Дата публикации
- 22.12.2021
- Всего подписок
- 6
- Всего просмотров
- 114
Библиография
- 1. Guba K. (2018) Big Data in Sociology: New Data, New Sociology? Sotsiologicheskoe obozreniye [Russian Sociological Review]. No. 1: 213–236. (In Russ.)
- 2. Deviatko I. F. (2016) From “Virtual Lab” to “Social Telescope”: Metaphors of Theoretical and Methodological Innovations in Online Research. In: Shashkin A. V., Deviatko I. F., Davydov S. G. (eds) Online-research in Russia: Trends and Prospects. Moscow: Tipografiya: 19–33. (In Russ.)
- 3. Dudina V. I., Iudina D. I. (2017) Mining Opinions on the Internet: Can Text Analysis Methods Replace Public Opinion Polls? Monitoring obshchestvennogo mneniya: ekonomicheskiye i sotsialnye peremeny [Monitoring of Public Opinion: Economic and Social Change]. No. 5: 63–78. (In Russ.). DOI: 10.14515/monitoring.2017.5.05.
- 4. Latour B. (2014) Reassembling the Social. An Introduction to Actor-Network-Theory. Moscow: NIU VShE. (In Russ.)
- 5. Tarde G. (2011) Laws of Imitation. Moscow: Akademicheskiy proekt. (In Russ.)
- 6. Tarde G. (2016) Monadology and Sociology. Perm: Gile Press. (In Russ.)
- 7. Achim E., Wolff T., Montagne D., Bail C. (2020) Computational Social Science and Sociology. Annual Review of Sociology. No. 46: 61–81. DOI: 10.1146/annurev-soc‑121919054621. Bail C. (2014) The Cultural Environment: Measuring Culture with Big Data. Theory and Society. Vol. 43. No. 3–4: 465–482. DOI: 10.1007/s11186-014-9216-5.
- 8. Boullier D. (2016) Big Data Challenges for the Social Sciences: From Society and Opinion to Replications. arXiv.org. July 18. URL: https://arxiv.org/abs/1607.05034 (accessed 30.08.21).
- 9. Boullier D. (2019) Replications in Quantitative and Qualitative Methods: a New Era for Commensurable Digital Social Sciences. arXiv.org. February 15. URL: https://arxiv.org/abs/1902.05984v1 (accessed 30.08.21).
- 10. Bowker G. C. (2014) The Theory/Data Thing Commentary. International Journal of Communication. Vol. 8. Article no. 2043: 1795–1799.
- 11. Centola D. (2010) The Spread of Behavior in an Online Social Network Experiment. Science. Vol. 329. No. 5996: 1194–1197. DOI: 10.1126/science.1185231.
- 12. Centola D. (2018) How Behavior Spreads: The Science of Complex Contagions. Princeton: Princeton Univ. Press. Crumley C. L. (2015) Heterarchy. In: Scott R. A., Buchmann M. C. (eds) Emerging Trends in the Social and Behavioral Sciences: An Interdiscplinary, Searchable, and Linkable Resource. Hoboken, NJ: Wiley: 1–14.
- 13. DiMaggio P., Nag M., Blei D. (2013) Exploiting Affinities between Topic Modeling and the Sociological Perspective on Culture: Application to Newspaper Coverage of U. S. Government Arts Funding. Poetics. Vol. 41. No. 6: 570–606. DOI: 10.1016/j.poetic.2013.08.004.
- 14. Ignatow G. (2016) Theoretical Foundations for Digital Text Analysis. Journal for the Theory of Social Behaviour. Vol. 46. No. 1: 104–120. DOI: 10.1111/jtsb.12086.
- 15. Latour B. (2002) Gabriel Tarde and the End of the Social. In: Joyce P. (ed.) The Social in Question: New Bearings in the History and the Social Sciences. London: Routledge.
- 16. Latour B. (2010) Tarde’s Idea of Quantification. In: Candea M. (ed.) The Social after Gabriel Tarde: Debates and Assessments (Culture, Economy and the Social). Abingdon: Routledge: 145–163.
- 17. Latour B., Jensen P., Venturini T., Grauwin S., Boullier D. (2012) ‘The Whole is Always Smaller than Its Parts’: A Digital Test of Gabriel Tarde’s Monads. The British Journal of Sociology. Vol. 63. No. 4: 591–615. DOI: 10.1111/j.1468–4446.2012.01428.x.
- 18. Ledford H. (2020). How Facebook, Twitter and Other Data Troves are Revolutionizing Social Science. Nature. No. 7812: 328–330. DOI: 10.1038/d41586020017471.
- 19. Marres N. (2017) Digital Sociology: The Reinvention of Social Research. Cambridge: Polity Press.
- 20. McFarland D., Lewis K., Goldberg A. (2016) Sociology in the Era of Big Data: The Ascent of Forensic Social Science. The American Sociologist. Vol. 47. No. 1: 12–35. DOI: 10.1007/s12108-015-9291-8.
- 21. Zhang J., Centola D. (2019) Social Networks and Health: New Developments in Diffusion, Online and Offline. Annual Review of Sociology. Vol. 45: 91–109. DOI: 10.1146/annurev-soc‑073117041421.
