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The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

Author

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  • Irina Evgenievna Kalabikhina

    (Population Department, Faculty of Economics, Lomonosov Moscow State University, Leninskije Gory, GSP-1, 119991 Moscow, Russia)

  • Evgeniy Petrovich Banin

    (Department of Applied Mechanics, Faculty of Robotics and Complex Automation, Bauman Moscow State Technical University, Baumanskaya 2-ya st., 5/1, 105005 Moscow, Russia)

  • Imiliya Abduselimovna Abduselimova

    (Population Department, Faculty of Economics, Lomonosov Moscow State University, Leninskije Gory, GSP-1, 119991 Moscow, Russia)

  • German Andreevich Klimenko

    (Population Department, Faculty of Economics, Lomonosov Moscow State University, Leninskije Gory, GSP-1, 119991 Moscow, Russia)

  • Anton Vasilyevich Kolotusha

    (Population Department, Faculty of Economics, Lomonosov Moscow State University, Leninskije Gory, GSP-1, 119991 Moscow, Russia)

Abstract

Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:987-:d:544877
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    References listed on IDEAS

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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. Chmiel, Anna & Sobkowicz, Pawel & Sienkiewicz, Julian & Paltoglou, Georgios & Buckley, Kevan & Thelwall, Mike & Hołyst, Janusz A., 2011. "Negative emotions boost user activity at BBC forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2936-2944.
    3. Irina E. Kalabikhina & Evgeny P. Banin, 2020. "Database "Pro-family (pronatalist) communities in the social network VKontakte"," Population and Economics, ARPHA Platform, vol. 4(3), pages 98-103, December.
    4. Irina E. Kalabikhina & Evgeny P. Banin, 2021. "Database "Childfree (antinatalist) communities in the social network VKontakte"," Population and Economics, ARPHA Platform, vol. 5(2), pages 92-96, July.
    5. 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.
    6. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. 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.

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