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On content analysis of images of mass protests: a case of data triangulation


The article discusses the methodological issues related to the content analysis of visual records of mass protests. Two categories of visual records are differentiated and compared: media coverage (documentary photography) and images from private collections (street photography). A sample of 382 images taken of the December 24, 2011 demonstration in Moscow, Russia is used for the purposes of the content analysis. The outcomes are compared with results of a survey administered among the protesters (\(N\)=791). It is argued that street photography produces a more valid visual account of the protest. The content analysis of visual records can complement the other methods for studying mass protests (survey research, qualitative in-depth interviews, participant observation, and network analysis of the social networking sites), particularly if no other data is available.

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  1. 1.

    The development of automated face recognition technologies raises concerns as to the protection of the privacy of individuals whose pictures are freely available on the internet (Risen and Poitras 2014).

  2. 2.

    The agreement criterion used is the frequency of codes in case, which complicates the task of reaching an acceptable level of agreement compared with a less stringent—yet more commonly reported one (the presence or absence of codes in case).

  3. 3.

    The code book had initially contained separate codes corresponding to five age groups: 20 years old and younger, 21–30 years old, 31-40 years old, 41–50 years old and 51 plus years old; they were subsequently transformed into four age groups: 24 years old and younger, 25–39 years old, 40–54 years old, 55 plus years old.

  4. 4.

    A possible explanation may refer to the unwillingness to disclose personal information, including demographic data, on-line that characterizes collectivistic cultures (Cho and Park 2013, pp. 2325–2326). Russian culture has several collectivistic features. The other explanation refers to a low level of the validity of personal information posted on-line in general.

  5. 5.

    The respondent could select several options when answering a survey question about his/her ideological affinities, hence the total count in the “Survey” column exceeds 100 %.

  6. 6.

    A binary logistic regression analysis shows that the supporters of nationalist ideas tend to be relatively younger and less educated than the other protesters. The outcome variable is a stated affinity with nationalist ideas (0 = No stated affinity, 1 = Stated affinity). Six predictor variables were included in the model. The first refers to the respondent’s gender (0 for females, 1 for males). The second refers to the respondent’s age at the scale level of measurement. The third refers the respondent’s education (0 for people with no university education, 1 for those with some university education). The fourth is a response to the survey question about whether or not the respondent learned about the December 24 demonstration from the internet (0 for those who did not and 1 for those who did). The fifth refers to the respondent’s relative well-being (0 for people who cannot afford to buy a car, 1 for those who can). The sixth refers to the respondent’s liberal values (0 for people who do not identify themselves as liberals, 1 for those who do). The binary regression logistic regression procedure in SPSS was used to perform the analysis. The method for entering predictors was Forward Stepwise (Likelihood Ratio). In Step 1, Age was entered. It produced a Cox and Snell’s \(R^{2}\) increment of .02 and a Nagelkerke’s \(R^{2}\) increment of 0.055. In Step 2, Education was entered. It produced a Cox and Snell’s \(R^{2}\) increment of 0.01 and a Nagelkerke’s \(R^{2}\) increment of 0.025. A test of the full model with the two predictor variables, Age and Education, compared with a constant-only or null model was statistically significant, \(\chi ^{2}(2)=24.105,\, p<.001\). The strength of the association between, on the one hand, the affinity with nationalist ideas and, on the other hand, Age and Education, was not strong with Cox and Snell’s \(R^{2}=0.03\) and Nagelkerke’s \(R^{2}=0.082\). The sign of B for Age is negative, that for Education is positive. In other words, the relative prevalence of older protesters suggested by the survey could have resulted from the relative underrepresentation of young protesters with nationalist ideas in its sample.

  7. 7.

    They were expressed by one of the reviewers.


  1. Arendt, H.: On Violence. Harcourt Brace, New York (1969)

    Google Scholar 

  2. Attia, A.M., Aziz, N., Friedman, B., Elhusseiny, M.F.: Commentary: the impact of social networking tools on political change in Egypt’s “Revolution 2.0”. Electron. Commer. Res. Appl. 10, 369–374 (2011)

    Article  Google Scholar 

  3. Bernard, R.H., Ryan, G.W.: Analyzing Qualitative Data: Systematic Approaches. Sage, Los Angeles (2010)

    Google Scholar 

  4. Bessinger, M.R.: Mechanisms of Maidan: the structure of contingency in the making of the Orange revolution. Mobilization 16(1), 25–43 (2011)

    Google Scholar 

  5. Brannen, J.: Mixing methods: the entry of qualitative and quantitative approaches into the research process. Int. J. Soc. Res. Methodol. 8(3), 173–184 (2005)

    Article  Google Scholar 

  6. Cho, S.E., Park, H.W.: Comparative analysis of Twitter use between South Koreans and Russians: an exploratory study. J. Korean Data Anal. Soc. 14(4), 1827–1838 (2012)

    Google Scholar 

  7. Cho, S.E., Park, H.W.: A qualitative analysis of cross-cultural new media research: SNS use in Asia and the West. Qual. Quant. 47(4), 2319–2330 (2013)

    Article  Google Scholar 

  8. Clément, C., Miryasova, O., Demidov, A.: Ot obyvatelei k aktivistam: zarozhdayushchiesya sotsial’nye dvizheniya v sovremennoi Rossii. Tri kvadrata, Moscow (2010)

  9. Fahmy, S., Kim, D.: Picturing the Iraq War: constructing the image of war in the British and US press. Int. Commun. Gaz. 70(6), 443–462 (2008)

    Article  Google Scholar 

  10. Fielding, N.: Mixed methods research in the real world. Int. J. Soc. Res. Methodol. 13(2), 127–138 (2010)

    Article  Google Scholar 

  11. Heuer, C.A., McClure, K.J., Puhl, R.M.: Obesity stigma in online news: a visual analysis. J. Health Commun. 16(9), 976–987 (2011)

    Article  Google Scholar 

  12. Hofheinz, A.: Nextopia? Beyond revolution 2.0. Int. J. Commun. 5, 1417–1434 (2011)

    Google Scholar 

  13. Jick, T.D.: Mixing qualitative and quantitative methods: triangulation in action. Adm. Sci. Quart. 24(4), 602–611 (1979)

  14. Kolářová, M.: Gender representation of the anti-globalization movement in the alternative media. Czech Sociol. Rev. 40(6), 851–868 (2004)

    Google Scholar 

  15. Krippendorff, K.: Content Analysis: An Introduction to Its Methodology, 2nd edn. Sage, Los Angeles (2004)

    Google Scholar 

  16. Kujawski, B., Abell, P.: Virtual communities? The Middle East revolutions at the Guardian forum: Comment Is free. Eur. Phys. J. B 83, 525–529 (2011)

    Article  Google Scholar 

  17. Ma, A., Norwich, B.: Triangulation and theoretical understanding. Int. J. Soc. Res. Methodol. 10(3), 173–184 (2007)

    Article  Google Scholar 

  18. O’Connell, R.: The use of visual methods with children in a mixed methods study of family food practices. Int. J. Soc. Res. Methodol. 16(1), 31–46 (2013)

    Article  Google Scholar 

  19. Oleinik, A.: Mixing quantitative and qualitative content analysis: triangulation at work. Qual. Quant. 45(4), 859–873 (2011)

  20. Oleinik, A.: Institutional exclusion as a destabilizing factor: the mass unrest of July 1, 2008 in Mongolia. Centr. Asian Surv. 31(2), 153–174 (2012)

  21. Oleinik, A., Popova, I., Kirdina, S., Shatalova, T.: On the choice of measures of reliability and validity in the content-analysis of texts. Qual. Quant. 48(5), 2703–2718 (2013)

  22. Onwuegbuzie, A.J., Leech, N.L.: On becoming a pragmatic researcher: the importance of combining quantitative and qualitative research Methodologies. Int. J. Soc. Res. Methodol. 8(5), 375–387 (2005)

    Article  Google Scholar 

  23. Opp, K.-D.: The production of historical “Facts”: how the wrong number of participants in the Leipzig monday demonstration on October 9, 1989 Became a Convention. Jahrbücher für Nationalökonomie Statistik 231(5–6), 598–607 (2011)

    Google Scholar 

  24. Ozel, B., Park, H.W.: Online image content analysis of political figures: an exploratory study. Qual. Quant. 46(4), 1013–1024 (2012)

    Article  Google Scholar 

  25. Perlesz, A., Lindsay, J.: Methodological triangulation in researching families: making sense of dissonant data. Int. J. Soc. Res. Methodol. 6(1), 25–40 (2003)

    Article  Google Scholar 

  26. Prosser, J., Schwartz, D.: Photographs within the sociological research process. In: Prosser, B. (ed.) Image-Based Research: A Sourcebook for Qualitative Researchers, pp. 115–130. Falmer, London (2005)

    Google Scholar 

  27. RIA Novosti: Inzhener-geodezist podschital uchastnikov mitinga na prospekte Sakharova. ( (2011). Retrieved 8 Apr 2012

  28. Risen, J., Poitras L.: N.S.A. Collecting Millions of Faces From Web Images. The New York Times, May 31 (2014)

  29. Rose, G.: Visual Methodologies: An Introduction to Researching Visual Materials, 3rd edn. Sage, Los Angeles (2012)

    Google Scholar 

  30. The Economist: The value of friendship. February 4–10, 23–26 (2012)

  31. Touraine, A., Dubet, F., Wievorka, M., Strzelecki, J.: Solidarité. Fayard, Paris (1982)

    Google Scholar 

  32. van Aelst, P., Walgrave, S.: Who is that (wo)man in the street? From the normalisation of protest to the normalisation of the protester. Eur. J. Polit. Res. 39(4), 461–486 (2001)

  33. Walgrave, S., Bennett, W.L., Van Laer, J., Breunig, C.: Multiple engagements and network bridging in contentious politics: digital media use of protest participants. Mobilization 16(3), 325–349 (2011)

    Google Scholar 

  34. Walgrave, S., Verhulst, J.: Selection and response bias in protest surveys. Mobilization 16(2), 203–222 (2011)

    Google Scholar 

  35. Warner, R.M.: Applied Statistics: From Bivariate Through Multivariate Techniques. Sage, Thousand Oaks (2008)

    Google Scholar 

  36. Wieviorka, M., Dubet, F.: Touraine and the method of sociological intervention. In: Clark, J., Diani, M. (eds.) Alain Touraine, pp. 55–76. Falmer, London (1996)

    Google Scholar 

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The author is indebted to Prof. Lev Gudkov, Denis Volkov and Dr. Alexei Grazhdankin of Levada Center (Moscow) for providing him with a full copy of the dataset with outcomes of a survey of participants in the December 24, 2011 demonstration on Academician Sakharov Avenue in Moscow and for answering a number of his additional queries. An anonymous reviewer of Quality & Quantity made a number of constructive and helpful suggestions as to how to improve an earlier version of this article. The author also thanks Dr. Natalia Ryzhova, Tatyana Zhuravskaya and Anna Pavlishina of the Amur State University (Blagoveshchensk, Russia) for their involvement in coding a sub-sample of pictures taken during that event. Sheryl Curtis of Communications WriteTouch improved the style of this manuscript.

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Correspondence to Anton Oleinik.



Table 2 Code Book for the content analysis of visual records of the December 24, 2011 demonstration

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Oleinik, A. On content analysis of images of mass protests: a case of data triangulation. Qual Quant 49, 2203–2220 (2015).

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  • Content analysis
  • Visual records
  • Triangulation
  • Mass protests
  • Russia