Prospects of media monitoring in public opinion research (using the example of trust in the president)
Research Article
How to Cite
Ankudinov I.A. Prospects of media monitoring in public opinion research (using the example of trust in the president). Sociology: methodology, methods, mathematical modeling (Sociology: 4M). 2025. No. 61. P. 165-203. DOI: https://doi.org/10.19181/4m.2025.34.2.4 (in Russ.).
Abstract
The changing political mood of Russians is a constant subject of interest for sociological agencies. With the development of the Internet, conventional questionnaire research began to be supplemented by online surveys and, despite some skepticism, by social media mining. This article attempts to adjust an accidental web-sample so as to bring its estimates closer to representative omnibuses. We use media- and polling measures of trust in the president Putin to answer the questions: How far apart are these sources? Is it possible to bring their estimates closer using post- stratification weights? Has the relationship between them been disrupted by the onset of the Ukrainian conflict? Is this relationship – despite some absolute difference – stable over time, i.e. Is it possible to move from one indicator to another using linear transformations? The conclusions we reach based on the results of the statistical analysis are as follows: (1) Internet measurements based on average dictionary sentiment estimate trust in the president by 20 percentage points lower than surveys. (2) Adjusting the user sample for weights from the population census does not allow the estimates to be reconciled. (3) Clarification of estimates for separate networks and pollsters only confirms the detected difference. For some platforms this difference is more significant, which can be explained by the specifics of their audiences. (4) The “Ukrainian factor” had an ambiguous impact on the discrepancy between estimates. Tonality according to “Medialogia” records an increase in the gap, while Dostoevsky sees almost no difference between “before” and “after”. (5) The temporal dependence of offline and online estimates is no less ambiguous. Weighting the Internet sample gives some reason to consider the corresponding time series to be cointegrated, but this relationship does not appear in all tests.
Keywords:
pollsters, social media, weighting, opinion mining, text analysis, time series, presidential rating, public trust
References
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29. Tumasjan A., Sprenger T., Sandner P., Welpe I. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment, Proceedings of the international AAAI conference on web and social media, 2010, vol. 4, no. 1, p. 178-185. DOI: 10.1609/icwsm.v4i1.14009.
30. Vaccari C., Valeriani A., Barberá P., Bonneau R., Jost J.T., Nagler J., Tucker J. Social Media and Political Communication. A Survey of Twitter Users during the 2013 Italian General Election, Italian Political Science Review, 2013, vol. 43, no. 3, p. 381-410. DOI: 10.1426/75245.
31. Gayo-Avello D. “I Wanted to Predict Elections with Twitter and All I Got Was This Lousy Paper” A Balanced Survey on Election Prediction Using Twitter Data. arXiv preprint. 1204.6441. 2012. DOI: 10.48550/arXiv.1204.6441.
32. Gayo-Avello D. A Meta-Analysis of State-of-the-Art Electoral Prediction from Twitter Data, Social Science Computer Review, 2013, vol. 31, no. 6, p. 649-679. DOI: 10.1177/0894439313493979.
33. Drus Z., Khalid H. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review, Procedia Computer Science, 2019, vol. 161, p. 707-714. DOI: 10.1016/j.procs.2019.11.174.
34. Chauhan P., Sharma N., Sikka G. The Emergence of Social Media Data and Sentiment Analysis in Election Prediction, Journal of Ambient Intelligence and Humanized Computing, 2021, vol. 12, p. 2601-2627. DOI: 10.1007/s12652-020-02423-y.
35. Oliveira D.J.S., Bermejo P.H.S., dos Santos P.A. Can Social Media Reveal the Preferences of Voters? A Comparison between Sentiment Analysis and Traditional Opinion Polls, Journal of Information Technology & Politics, 2016, vol. 14, no. 1, p. 34-45. DOI: 10.1080/19331681.2016.1214094.
36. Cheng J., Zhang X., Li P., Zhang S., Ding Z., Wang H. Exploring Sentiment Parsing of Microblogging Texts for Opinion Polling on Chinese Public Figures, Applied Intelligence, 2016, vol. 45, p. 429-442. DOI: 10.1007/s10489-016-0768-0.
37. Maleki A. How Do Leading Methods Mislead? Measuring Public Opinions in Authoritarian Contexts, Proceedings of the IPSA, 2021, p. 1-31.
38. Frye T., Gehlbach S., Marquardt K.L., Reuter O.J. Is Putin’s Popularity Real? Post-Soviet Affairs, 2017, vol. 33, no. 1, p. 1-15. DOI: 10.1080/1060586X.2016.1144334.
39. Frye T., Gehlbach S., Marquardt K.L., Reuter O.J. Is Putin’s Popularity (Still) Real? A Cautionary Note on Using List Experiments to Measure Popularity in Authoritarian Regimes, Post-Soviet Affairs, 2023, vol. 39, no. 3, p. 213-222. DOI: 10.1080/1060586X.2023.2187195.
40. Hale H.E. Authoritarian Rallying as Reputational Cascade? Evidence from Putin’s Popularity Surge after Crimea, American Political Science Review, 2022, vol. 116, no. 2, p. 580-594. DOI: 10.1017/S0003055421001052.
41. Buckley N., Marquardt K.L., Reuter O.J., Tertytchnaya K. Endogenous Popularity: How Perceptions of Support Affect the Popularity of Authoritarian Regimes, American Political Science Review, 2024, vol. 118, no. 2, p. 1046-1052. DOI: 10.1017/S0003055423000618.
42. Šubrt J. Do Public Opinion Polls actually Provide a Survey of Public Opinion? (in Russian), Sociological Studies, 2018, no. 12, p. 56-65. DOI: 10.31857/S013216250003169-8.
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48. “Evaluating Survey Quality in Today’s Complex Environment. Social Media in Public Opinion Research: Executive Summary of the AAPOR Task Force on Emerging Technologies in Public Opinion Research” (transl., in Russian), in: American Association for Public Opinion Research. Moscow: VCIOM. 78 p.
49. Ankudinov I.A. Patriotic Discourse in Runet: Before and after February 24, 2022 (in Russian), Monitoring of Public Opinion: Economic and Social Changes, 2024, no. 2, p. 153-177. DOI: 10.14515/monitoring.2024.2.2515.
50. Antasheva M.S., Lobanova P.A., Isaeva I.K., Sabidaeva E.A., Piekalnits A.S., Loginova I.V. Sentiment analysis as an information agenda and public opinion research method (on the example of Chinese mass media and social networks) (in Russian), Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M), 2024, no. 2, p. 7-41. DOI: 10.19181/4m.2023.32.2.1.
51. Barberá P. Less Is More? How Demographic Sample Weights Can Improve Public Opinion Estimates Based on Twitter Data, NYU Working Paper, 2016, p. 1-37.
52. Hopkins D.J., King G. A Method of Automated Nonparametric Content Analysis for Social Science, American Journal of Political Science, 2010, vol. 54, no. 1, p. 229-247. DOI: 10.1111/J.1540-5907.2009.00428.X.
2. Kizilova K., Norris P. Assessing Russian Public Opinion on the Ukraine War, Russian Analytical Digest, 2022, no. 281, p. 2-5. DOI: 10.3929/ethz-b-000539633.
3. Volkov D., Rosenfeld B., Morris J., Pleines H., Biriukova A., Koneva E., Chilingaryan A., Miniailo A., Kamalov E., Sergeeva I., Zavadskaya M., Kostenko V. The Value of Public Opinion Polls, Russian Analytical Digest, 2023, no. 292, p. 1-21. DOI: 10.3929/ethz-b-000599408.
4. Best S.J., Krueger B.S. Analyzing the Representativeness of Internet Political Participation, Political Behavior, 2005, vol. 27, p. 183-216. DOI: 10.1007/S11109-005-3242-Y.
5. Chang L., Krosnick J.A. National Surveys via RDD Telephone Interviewing versus the Internet: Comparing Sample Representativeness and Response Quality, Public Opinion Quarterly, 2009, vol. 73, no. 4, p. 641-678. DOI: 10.1093/poq/nfp075.
6. Smetanin S. The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives, IEEE Access, 2020, vol. 8, p. 110693-110719. DOI: 10.1109/ACCESS.2020.3002215.
7. Manza J., Brooks C. How Sociology Lost Public Opinion: A Genealogy of a Missing Concept in the Study of the Political, Sociological Theory, 2012, vol. 30, no. 2, p. 89-113. DOI: 10.1177/0735275112448054.
8. Greene S.A., Robertson G.B. Putin v. The People: The Perilous Politics of a Divided Russia. New Haven: Yale University Press, 2019. 287 p. DOI: 10.2307/j.ctvfc5417.
9. Berinsky A.J. Measuring Public Opinion with Surveys, Annual Review of Political Science, 2017, vol. 20, no. 1, p. 309-329.
10. Glynn C.J., Herbst S., Lindeman M., O’Keefe G.J., Shapiro R.Y. “Methods for Studying Public Opinion”, in: Public Opinion. New York: Routledge, 2018, p. 57-86. DOI: 10.4324/9780429493256.
11. Larsen E.G., Fazekas Z. “Alternatives to Opinion Polls: No Polls, Vox Pop, Poll Aggregators and Social Media”, in: Reporting Public Opinion: How the Media Turns Boring Polls into Biased News. Cham: Palgrave Macmillan, 2021, p. 109-121. DOI: 10.1007/978-3-030-75350-4_6.
12. Ravi K., Ravi V. A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications, Knowledge-Based Systems, 2015, vol. 89 p. 14-46. DOI: 10.1016/j.knosys.2015.06.015.
13. Ceron A., Curini L., Iacus S.M., Porro G. Every Tweet Counts? How Sentiment Analysis of Social Media Can Improve Our Knowledge of Citizens’ Political Preferences with an Application to Italy and France, New Media & Society, 2014, vol. 16, no. 2, p. 340-358. DOI: 10.1177/1461444813480466.
14. Stukal D., Sanovich S., Bonneau R., Tucker J.A. Detecting Bots on Russian Political Twitter, Big data, 2017, vol. 5, no. 4, p. 310-324. DOI: 10.1089/big.2017.0038.
15. Media consumption and online activity (in Russian), in: VCIOM [site]. 23.09.2021. URL: https://wciom.ru/analytical-reviews/analiticheskii-obzor/mediapotreblenie-i-aktivnost-v-internete (date of access: 29.10.2025).
16. Gunter B., Koteyko N., Atanasova D. Sentiment Analysis: A Market-Relevant and Reliable Measure of Public Feeling? International Journal of Market Research, 2014, vol. 56, no. 2, p. 231-247. DOI: 10.2501/IJMR-2014-014.
17. Glazunova S. “The “Sovereign Internet” and Social Media”, in: Digital Activism in Russia: The Communication Tactics of Political Outsiders. Cham: Springer, 2022, p. 67-88. DOI: 10.1007/978-3-030-93503-0_4.
18. Skoric M.M., Zhu Q., Goh D., Pang N. Social Media and Citizen Engagement: A Meta-Analytic Review, New Media & Society, 2016, vol. 18, no. 9, p. 1817-1839. DOI: 10.1177/1461444815616221.
19. Bisbee J., Larson J.M. Testing Social Science Network Theories with Online Network Data: An Evaluation of External Validity, American Political Science Review, 2017, vol. 111, no. 3, p. 502-521. DOI: 10.1017/S0003055417000120.
20. Chae Y., Lee S., Kim Y. Meta-Analysis of the Relationship between Internet Use and Political Participation: Examining Main and Moderating Effects, Asian Journal of Communication, 2019, vol. 29, no. 1, p. 35-54. DOI: 10.1080/01292986.2018.1499121.
21. Glenski M., Weninger T. Rating Effects on Social News Posts and Comments, ACM Transactions on Intelligent Systems and Technology, 2017, vol. 8, no. 6, p. 1-19. DOI: 10.1145/2963104.
22. Feezell J.T., Conroy M., Guerrero M. Internet Use and Political Participation: Engaging Citizenship Norms through Online Activities, Journal of Information Technology & Politics, 2016, vol. 1, no. 2, p. 95-107. DOI: 10.1080/19331681.2016.1166994.
23. Schober M.F., Pasek J., Guggenheim L., Lampe C., Conrad F.G. Social Media Analyses for Social Measurement, Public Opinion Quarterly, 2016, vol. 80, no. 1, p. 180-211. DOI: 10.1093/POQ%2FNFV048.
24. Filatova O., Chugunov A., Bolgov R. “Transformation of the Electronic Participation System in Russia in the Early 2020s: Centralization Trends”, in: International Conference on Topical Issues of International Political Geography. Cham: Springer, 2021, p. 309-319. DOI:10.1007/978-3-031-20620-7_27.
25. Pekar V., Najafi H., Binner J. M., Swanson R., Rickard C., Fry J. Voting Intentions on Social Media and Political Opinion Polls, Government Information Quarterly, 2022, vol. 39, no. 4, p. 101658. DOI: 10.1016/j.giq.2021.101658.
26. Ouni S., Fkih F., Omri M.N. A Survey of Machine Learning-Based Author Profiling from Texts Analysis in Social Networks, Multimedia Tools and Applications, 2023, vol. 82, no. 24, p. 36653–36686. DOI: 10.1007/s11042-023-14711-8.
27. Cody E.M., Reagan A.J., Dodds P.S., Danforth C.M. Public Opinion Polling with Twitter. arXiv preprint. 1608.02024. 2016. DOI: 10.48550/arXiv.1608.02024.
28. DiGrazia J., McKelvey K., Bollen J., Rojas F. More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior, PloS One, 2013, vol. 8, no. 11, p. e79449. DOI: 10.1371/journal.pone.0079449.
29. Tumasjan A., Sprenger T., Sandner P., Welpe I. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment, Proceedings of the international AAAI conference on web and social media, 2010, vol. 4, no. 1, p. 178-185. DOI: 10.1609/icwsm.v4i1.14009.
30. Vaccari C., Valeriani A., Barberá P., Bonneau R., Jost J.T., Nagler J., Tucker J. Social Media and Political Communication. A Survey of Twitter Users during the 2013 Italian General Election, Italian Political Science Review, 2013, vol. 43, no. 3, p. 381-410. DOI: 10.1426/75245.
31. Gayo-Avello D. “I Wanted to Predict Elections with Twitter and All I Got Was This Lousy Paper” A Balanced Survey on Election Prediction Using Twitter Data. arXiv preprint. 1204.6441. 2012. DOI: 10.48550/arXiv.1204.6441.
32. Gayo-Avello D. A Meta-Analysis of State-of-the-Art Electoral Prediction from Twitter Data, Social Science Computer Review, 2013, vol. 31, no. 6, p. 649-679. DOI: 10.1177/0894439313493979.
33. Drus Z., Khalid H. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review, Procedia Computer Science, 2019, vol. 161, p. 707-714. DOI: 10.1016/j.procs.2019.11.174.
34. Chauhan P., Sharma N., Sikka G. The Emergence of Social Media Data and Sentiment Analysis in Election Prediction, Journal of Ambient Intelligence and Humanized Computing, 2021, vol. 12, p. 2601-2627. DOI: 10.1007/s12652-020-02423-y.
35. Oliveira D.J.S., Bermejo P.H.S., dos Santos P.A. Can Social Media Reveal the Preferences of Voters? A Comparison between Sentiment Analysis and Traditional Opinion Polls, Journal of Information Technology & Politics, 2016, vol. 14, no. 1, p. 34-45. DOI: 10.1080/19331681.2016.1214094.
36. Cheng J., Zhang X., Li P., Zhang S., Ding Z., Wang H. Exploring Sentiment Parsing of Microblogging Texts for Opinion Polling on Chinese Public Figures, Applied Intelligence, 2016, vol. 45, p. 429-442. DOI: 10.1007/s10489-016-0768-0.
37. Maleki A. How Do Leading Methods Mislead? Measuring Public Opinions in Authoritarian Contexts, Proceedings of the IPSA, 2021, p. 1-31.
38. Frye T., Gehlbach S., Marquardt K.L., Reuter O.J. Is Putin’s Popularity Real? Post-Soviet Affairs, 2017, vol. 33, no. 1, p. 1-15. DOI: 10.1080/1060586X.2016.1144334.
39. Frye T., Gehlbach S., Marquardt K.L., Reuter O.J. Is Putin’s Popularity (Still) Real? A Cautionary Note on Using List Experiments to Measure Popularity in Authoritarian Regimes, Post-Soviet Affairs, 2023, vol. 39, no. 3, p. 213-222. DOI: 10.1080/1060586X.2023.2187195.
40. Hale H.E. Authoritarian Rallying as Reputational Cascade? Evidence from Putin’s Popularity Surge after Crimea, American Political Science Review, 2022, vol. 116, no. 2, p. 580-594. DOI: 10.1017/S0003055421001052.
41. Buckley N., Marquardt K.L., Reuter O.J., Tertytchnaya K. Endogenous Popularity: How Perceptions of Support Affect the Popularity of Authoritarian Regimes, American Political Science Review, 2024, vol. 118, no. 2, p. 1046-1052. DOI: 10.1017/S0003055423000618.
42. Šubrt J. Do Public Opinion Polls actually Provide a Survey of Public Opinion? (in Russian), Sociological Studies, 2018, no. 12, p. 56-65. DOI: 10.31857/S013216250003169-8.
43. Trust in politicians (in Russian), in: VCIOM [site]. 2022. URL: https://wciom.ru/ratings/doverie-politikam/ (date of access: 29.10.2025).
44. Dominants. Field of Opinions. Issue 33 (in Russian), in: FOM [site]. 2022. URL: https://fom.ru/Dominanty/14770 (date of access: 29.10.2025).
45. Approval of authorities (in Russian), in: Levada-center [site]. 2022. URL: https://www.levada.ru/indikatory/odobrenie-organov-vlasti/ (date of access: 29.10.2025).
46. Bureaucratic Labs. Dostoevsky Sentiment analysis library for Russian language, in: Github [site]. 2021. URL: https://github.com/bureaucratic-labs/dostoevsky (date of access: 29.10.2025).
47. Dudina V.I., Iudina D.I. Mining Opinions on the Internet: Can the Text Analysis Methods Replace Public Opinion Polls? (in Russian), Monitoring of Public Opinion: Economic and Social Changes, 2017, no. 5, p. 63-78. DOI: 10.14515/monitoring.2017.5.05.
48. “Evaluating Survey Quality in Today’s Complex Environment. Social Media in Public Opinion Research: Executive Summary of the AAPOR Task Force on Emerging Technologies in Public Opinion Research” (transl., in Russian), in: American Association for Public Opinion Research. Moscow: VCIOM. 78 p.
49. Ankudinov I.A. Patriotic Discourse in Runet: Before and after February 24, 2022 (in Russian), Monitoring of Public Opinion: Economic and Social Changes, 2024, no. 2, p. 153-177. DOI: 10.14515/monitoring.2024.2.2515.
50. Antasheva M.S., Lobanova P.A., Isaeva I.K., Sabidaeva E.A., Piekalnits A.S., Loginova I.V. Sentiment analysis as an information agenda and public opinion research method (on the example of Chinese mass media and social networks) (in Russian), Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M), 2024, no. 2, p. 7-41. DOI: 10.19181/4m.2023.32.2.1.
51. Barberá P. Less Is More? How Demographic Sample Weights Can Improve Public Opinion Estimates Based on Twitter Data, NYU Working Paper, 2016, p. 1-37.
52. Hopkins D.J., King G. A Method of Automated Nonparametric Content Analysis for Social Science, American Journal of Political Science, 2010, vol. 54, no. 1, p. 229-247. DOI: 10.1111/J.1540-5907.2009.00428.X.
Article
Received: 30.01.2025
Accepted: 17.12.2025
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Ankudinov, I. A. (2025). Prospects of media monitoring in public opinion research (using the example of trust in the president). Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M), (61), 165-203. https://doi.org/10.19181/4m.2025.34.2.4
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