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Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki

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  • Sergey Smetanin

    (Department of Business Informatics, Graduate School of Business, National Research University Higher School of Economics, 101000 Moscow, Russia)

Abstract

Policymakers and researchers worldwide are interested in measuring the subjective well-being (SWB) of populations. In recent years, new approaches to measuring SWB have begun to appear, using digital traces as the main source of information, and show potential to overcome the shortcomings of traditional survey-based methods. In this paper, we propose the formal model for calculation of observable subjective well-being (OSWB) indicator based on posts from a social network, which utilizes demographic information and post-stratification techniques to make the data sample representative by selected characteristics of the general population. We applied the model on the data from Odnoklassniki, one of the largest social networks in Russia, and obtained an OSWB indicator representative of the population of Russia by age and gender. For sentiment analysis, we fine-tuned several language models on RuSentiment and achieved state-of-the-art results. The calculated OSWB indicator demonstrated moderate to strong Pearson’s ( r = 0.733 , p = 0.007 , n = 12 ) correlation and strong Spearman’s ( r s = 0.825 , p = 0.001 , n = 12 ) correlation with a traditional survey-based Happiness Index reported by Russia Public Opinion Research Center, confirming the validity of the proposed approach. Additionally, we explored circadian (24 h) and circaseptan (7 day) patterns, and report several interesting findings for the population of Russia. Firstly, daily variations were clearly observed: the morning had the lowest level of happiness, and the late evening had the highest. Secondly, weekly patterns were clearly observed as well, with weekends being happier than weekdays. The lowest level of happiness occurs in the first three weekdays, and starting on Thursday, it rises and peaks during the weekend. Lastly, demographic groups showed different levels of happiness on a daily, weekly, and monthly basis, which confirms the importance of post-stratification by age group and gender in OSWB studies based on digital traces.

Suggested Citation

  • Sergey Smetanin, 2022. "Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki," Mathematics, MDPI, vol. 10(16), pages 1-38, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2947-:d:888837
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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Krueger, Alan B. & Schkade, David A., 2008. "The reliability of subjective well-being measures," Journal of Public Economics, Elsevier, vol. 92(8-9), pages 1833-1845, August.
    3. José Luis Ayuso-Mateos & Marta Miret & Francisco Félix Caballero & Beatriz Olaya & Josep Maria Haro & Paul Kowal & Somnath Chatterji, 2013. "Multi-Country Evaluation of Affective Experience: Validation of an Abbreviated Version of the Day Reconstruction Method in Seven Countries," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.
    4. Grishchenko, Natalia, 2020. "The gap not only closes: Resistance and reverse shifts in the digital divide in Russia," Telecommunications Policy, Elsevier, vol. 44(8).
    5. Fabio Sabatini & Francesco Sarracino, 2017. "Online Networks and Subjective Well-Being," Kyklos, Wiley Blackwell, vol. 70(3), pages 456-480, August.
    6. Robert Costanza & Ida Kubiszewski & Enrico Giovannini & Hunter Lovins & Jacqueline McGlade & Kate E. Pickett & Kristín Vala Ragnarsdóttir & Debra Roberts & Roberto De Vogli & Richard Wilkinson, 2014. "Development: Time to leave GDP behind," Nature, Nature, vol. 505(7483), pages 283-285, January.
    7. John F Helliwell & Shun Wang, 2015. "How Was the Weekend? How the Social Context Underlies Weekend Effects in Happiness and Other Emotions for US Workers," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
    8. Peng Nie & Alfonso Sousa-Poza & Galit Nimrod, 2017. "Internet Use and Subjective Well-Being in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 132(1), pages 489-516, May.
    9. Sergei Monakhov, 2020. "Early detection of internet trolls: Introducing an algorithm based on word pairs / single words multiple repetition ratio," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    11. Marc Fleurbaey, 2009. "Beyond GDP: The Quest for a Measure of Social Welfare," Journal of Economic Literature, American Economic Association, vol. 47(4), pages 1029-1075, December.
    12. Irina Evgenievna Kalabikhina & Evgeniy Petrovich Banin & Imiliya Abduselimovna Abduselimova & German Andreevich Klimenko & Anton Vasilyevich Kolotusha, 2021. "The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte," Mathematics, MDPI, vol. 9(9), pages 1-25, April.
    13. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    14. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    15. K. Levin & C. Currie, 2014. "Reliability and Validity of an Adapted Version of the Cantril Ladder for Use with Adolescent Samples," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(2), pages 1047-1063, November.
    16. N. Wang & M. Kosinski & D. Stillwell & J. Rust, 2014. "Can Well-Being be Measured Using Facebook Status Updates? Validation of Facebook’s Gross National Happiness Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 115(1), pages 483-491, January.
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