Political Content of Social Movements in the Online Space of Modern States: Methodology of the Analysis and Research Practices | South-Russian Journal of Social Sciences
Political Content of Social Movements in the Online Space of Modern States: Methodology of the Analysis and Research Practices
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https://doi.org/10.31429/26190567-19-3-139-162
https://doi.org/10.31429/26190567-19-3-139-162

How to Cite Array

Ryabchenko N.A., Katermina V.V., Gnedash A.A., Malysheva O.P. (2018) Political Content of Social Movements in the Online Space of Modern States: Methodology of the Analysis and Research Practices. South-Russian Journal of Social Sciences, 19 (3), pp. 139-162. DOI: 10.31429/26190567-19-3-139-162 (In Russian)
Submission Date 2018-08-04
Accepted Date 2018-09-03
Published Date 2018-09-27

Copyright (c) 2018 Наталья Анатольевна Рябченко, Вероника Викторовна Катермина, Анна Александровна Гнедаш, Ольга Петровна Малышева

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The online space, which is characterized by boundless opportunities of communication, as well as the ways to restrict information flows, nowadays is the basic ground for constructing and functioning of different social movements, including their function of producing political content. These movements due to their active (constructive and/ or deconstructive) online and offline activity cannot be studied with classical methods of analysis. Our study focuses on the development of the three-stage methodology of political content research, produced in the online space by current social movements. The developed original methodology includes the use of structural network analysis (quantitative research and modelling of a social movement in the form of a social graph), relational analysis (qualitative analysis of network data of the constructed social graph), linguistic and discourse analysis (implications, discourses, repertoires and norms of a social movement). An approbation of the three-stage methodology of the analysis is performed on the basis of the research of the one of the most massive (global, regional and local framework) and proactive social movements of the present time — the Women’s March Movement. We used structural network analysis of network data in Twitter, a social microblogging service, sampled from January to June 2018 to construct a network model of the Women’s March Movement and visualize it as a social graph. We applied relational analysis to the constructed graph to identify and describe the discussion core «Women’s March», which is the aggregate of clusters, determining the main directions of the discourse of the Women’s March Movement. Linguistic and discourse analysis of the sampled clusters was applied as semantic and discourse analysis of tags and hashtags (folksonomy analysis) of the constructed discussion core of the political content, produced by the Women’s March social movement. The applied methodology provides a range of research opportunities: to analyze any social movement as regards its stability as a network entity; to visualize the structure of the movement (actors, sub-communities, core, peripherals, participants, proponents, content consumers); to identify qualitative characteristics, which determine interactions inside the community/ movement as well as interactions with the outside environment; to determine potential points of growth and vectors of social movement transformation both in the online and offline space; to predict further development of the movements and the nature of its activities both in the online and offline space.

Keywords

political content, social movements, network communities, structural network analysis, relational analysis, linguistic and discourse analysis, hashtags, the online space, social media

Acknowledgements

The study was carried out through the financial support of The Russian Foundation for Basic Research (Department of Humanitarian and Social Science), the research project № 18–011–00910 entitled “The models and practices of political content management in modern states’ online space in The Post-Truth Era” (2018–2020, supervised by N. A. Ryabchenko).

References

  1. About the EVAW Coalition. (2018). Retrieved from http://www.endviolenceagainstwomen.org.uk/about/
  2. About Women’s March. (2018). Retrieved from http://www.womensmarch.com/mission/
  3. Alter, C. A Year Ago, They Marched. Now a Record Number of Women Are Running for Office. (2018, January 29). Time. Режим доступа http://time.com/5107499/record-number-of-women-are-running-for-office/
  4. Barabasi, A.-L. (2009). Scale-Free Networks: A Decade and Beyond. Science, 325, 412–413.
  5. Bonzanini, M. (2016). Mastering Social Media Mining with Python. Birmingham-Mumbai: PACKT Publishing.
  6. Cauterucci, C. Getting the Women’s March on Washington on the Road. (2016, November 23). Slate. Retrieved from http://www.slate.com/articles/life/doublex/2016/11/the_women_s_march_on_washington_faces_uncertain_logistics_on_inauguration.html?via=gdpr-consent
  7. Emirbayer, M. (1997). Manifesto for a relational sociology. American Journal of Sociology, 2 (103), 281–317.
  8. Erikson, E. (2013). Formalist and relationalist theory in social network analysis. Sociological Theory, 3 (31), 219–242.
  9. European Development Days. (2018). Retrieved from https://eudevdays.eu/
  10. EVAW Campaigns. (2018). Retrieved from http://www.endviolenceagainstwomen.org.uk/campaign/
  11. Fairclough, N. (2003). The Dialectics of Discourse: Textual Analysis for Social Research. London: Routledge.
  12. Feldt, G. How Companies Must Adapt in the #MeToo Era. (2018, January 29). Time. Режим доступа http://time.com/5120607/companies-leadership-metoo-era/
  13. Galyamina, Yu. E. (2014). Lingvisticheskij analiz heshtegov Tvittera [Linguistic Analysis of Twitter Hashtags]. In Ya. E. Akhapkina & E. V. Rakhilina (Ed.) Sovremennyj russkij jazyk v internete [Modern Russian on the Internet] (pp. 13–22). Moscow: Jazyki slavjanskoj kul’tury.
  14. Gee, P. J. (2001). An Introduction to Discourse Analysis. Theory and Method. London, New York: Taylor & Francis e-Library.
  15. Gnedash, A. A., & Ryabchenko, N. A. (2018). Setevoj analiz sovremennyh protestnyh dvizhenij (na primere social’noj seti “Womensmarch”) [Network Analysis of Modern Protest Movements (on the Example of the Social Network “Womensmarch”)]. In Sociolog 2.0: Transformacija professii (Materialy VIII mezhdunarodnoj sociologicheskoj Grushinskoj konferencii) [Sociologist 2.0: Transformation of the Profession (Materials of the VIII International Sociological Grushin Conference) (pp. 404–409). Moscow: AO “VCIOM”.
  16. Gnedash, A. A. (2015). Aktory semejnoj politiki v informacionno-novostnom pole online-prostranstva sovremennoj Rossii: analiz folksonomii [Actors of Family Policy in the News and Information Field of the Online Space of Modern Russia: the Analysis of Folksonomy]. In Social’nyj komp’juting: osnovy, tehnologii razvitija, social’no-gumanitarnyej effekty (Materialy Chetvertoj Mezhdunarodnoj nauchno-prakticheskoj konferencii) [Social Computing: Fundamentals, Technologies of Development, Social and Humanitarian Effects (Materials of the Fourth International Scientific and Practical Conference)] (pp. 31–36). Moscow: MPGU.
  17. Harari, F. (2006). Teorija grafov [Graph Theory]. Moscow: URSS.
  18. Heyboer, K. (2017). Women’s March on Washington 2017: Who’s going and when, how to get there and why it’s happening. Retrieved from http://www.nj.com/news/index.ssf/2017/01/womens_march_washington_2017_dc_trump_when.html
  19. Hjeshtegi: chto jeto I zachem ispol’zovat’? [Hashtags: What is it and Why Use it?]. Retrieved from http://wiki.rookee.ru/Xeshteg
  20. In 1 second, each and every second, there are… (2018). Retrieved from http://www.internetlivestats.com/one-second
  21. Kahn, E. V. (2017). Hjeshtegi kak novoe lingvisticheskoe javlenie [Hashtags as a New Linguistic Phenomenon]. Filologicheskij aspect [Philological Aspect], 1 (21), 91–98.
  22. Katermina, V. V. (2017). Kul’turno-interpretirujushchij potencial massmedijnyh neologizmov v anglijskom diskurse [Cultural-Interpretive Potential of Mass Media Neologisms in English Discourse]. Voprosy kognitivnoj lingvistiki [Issues of Cognitive Linguistics], 1, 84–90.
  23. Knoke, D., & Yang, S. (2008). Network analysis. Indiana University: Sage.
  24. Malone, S., & Gibson, G. (2017). In challenge to Trump, women protesters swarm streets across U. S. Retrieved from http://www.reuters.com/article/us-usa-trump-women/in-challenge-to-trump-women-protesters-swarm-streets-across-u-s-idUSKBN1550DW
  25. Martin, A., & Wellman, B. (2011). Social Network Analysis: An Introduction. In P. Carrington, J. Scott (Eds.) Handbook of Social Network Analysis (pp. 11–25). Thousand Oaks, CA: Sage.
  26. More than 12M “MeToo” Facebook posts, comments, reactions in 24 hours. (2017, October 17). CBS News. Retrieved from http://www.cbsnews.com/news/metoo-more-than-12-million-facebook-posts-comments-reactions-24-hours/
  27. Nevada Senate Election Results. (2018). Politico. Retrieved from http://www.politico.com/2016-election/results/map/senate/nevada/
  28. Nikolaeva, M. V., & Romanova, Yu. A. (2018). Prezidentskaja predvybornaja kampanija 2018 goda: folksonomija i analiz kommentariev elektorata v onlajn-prostranstve [Presidential Election Campaign of 2018: Folksonomy and Analysis of Comments of the Electorate in the Online Space]. In Sociolog 2.0: Transformacija professii (Materialy VIII mezhdunarodnoj sociologicheskoj Grushinskoj konferencii) [Sociologist 2.0: Transformation of the Profession (Materials of the VIII International Sociological Grushin Conference) (pp. 367–371). Moscow: AO “VCIOM”.
  29. Power to the Polls. (2018). Retrieved from http://www.powertothepolls.com
  30. Przybyla, H. M., & Schouten, F. (2017). At 2.5 million strong, Women’s Marches crush expectations. USA Today. Retrieved from http://www.usatoday.com/story/news/politics/2017/01/21/womens-march-aims-start-movement-trump-inauguration/96864158/
  31. Respers, L. (2017). #MeToo: Social media flooded with personal stories of assault. CNN entertainment. Retrieved from https://edition.cnn.com/2017/10/15/entertainment/me-too-twitter-alyssa-milano/index.html
  32. Rife, K. (2017). An incomplete, depressingly long list of celebrities’ sexual assault and harassment stories. A. V. Club. Retrieved from http://www.avclub.com/an-incomplete-depressingly-long-list-of-celebrities-se-1819628519
  33. Ryabchenko, N. A., & Malysheva, O. P. (2018). Upravlenie politicheskim kontentom: semanticheskij analiz tegov I hjeshtegov [Management of Political Content: Semantic Analysis of Tags and Hashtags]. In Sociolog 2.0: Transformacija professii (Materialy VIII mezhdunarodnoj sociologicheskoj Grushinskoj konferencii) [Sociologist 2.0: Transformation of the Profession (Materials of the VIII International Sociological Grushin Conference) (pp. 362–367). Moscow: AO “VCIOM”.
  34. Senators say #MeToo: McCaskill, others share their stories of sexual harassment. (2017, October 21). The Washington Post. Retrieved from http://www.washingtonpost.com/news/powerpost/wp/2017/10/21/senators-say-metoo-mccaskill-others-share-their-stories-of-sexual-harassment/?noredirect=on&utm_term=.fc610ef7ecc4
  35. Shchurina, Yu. V. (2015). Komunikativno-igrovoj potencial hjeshtegov [The Communicative and Playful Potential of Hashtags]. Vestnik Cherepoveckogo gosudarstvennogo universiteta [Cherepovets State University Bulletin], 8, 100–104.
  36. Shugerman, E. MeToo: Why are women sharing stories of sexual assault and how did it start? (2017). Independent. Retrieved from https://www.independent.co.uk/news/world/americas/me-too-facebook-hashtag-why-when-meaning-sexual-harassment-rape-stories-explained-a8005936.html
  37. Smorgunov, L. V. (Ed.) (2015). Upravlenie publichnoj politikoj [Public Policy Management]. Moscow: Izdatel’stvo “Aspekt Press”.
  38. Solis, B. (2011). The Hashtag Economy. Retrieved from http://www.briansolis.com/2011/06/hashtag-this-the-culture-of-social-media-is/
  39. Szomszor, M., Alani, H., Cantador, I., O’Hara, K. & Shadbolt, N. (2018). Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis. Retrieved from https://pdfs.semanticscholar.org/5949/f09003129baec05cf7efb6a0a9539bd4c615.pdf
  40. Tambuscio, M., Guffo, G., Flammini, A. & Menczer, F. (2015). Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks. In Proceedings of the 24th International Conference on World Wide Web Companion, 977–982. Retrieved from https://dl.acm.org/citation.cfm?id=2740908
  41. The Harvey Weinstein effect. (2018, February 1). USA Today. Retrieved from http://www.usatoday.com/pages/interactives/life/the-harvey-weinstein-effect
  42. Tiefenthäler, A. Women’s March 2018: Thousands of Protesters Take to the Streets. (2018, January 20). The New York Times. Retrieved from http://www.nytimes.com/2018/01/20/us/womens-march.html
  43. Tolentino, J. The Somehow Controversial Women’s March on Washington. (2017, January 18). The New Yorker. Режим доступа http://www.newyorker.com/culture/jia-tolentino/the-somehow-controversial-womens-march-on-washington
  44. UN Women Annual Report. (2018). Retrieved from http://annualreport.unwomen.org/en/2018
  45. Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge: Cambridge University Press.
  46. Women’s March focuses on voter registration at Las Vegas event. (2017). Retrieved from http://www.pbs.org/newshour/show/womens-march-focuses-on-voter-registration-at-las-vegas-event