IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0325.html
   My bibliography  Save this article

Social network sites: What users post and to whom they address. Some approaches to the study

Author

Listed:
  • Kotyrlo , Elena

    (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation;)

Abstract

Study of users and their segmentation, based on users’ preferred topics of discussion and their networking, is the unique opportunity offered by social networks. Variety of approaches to social media analysis based on social network analysis and text mining is summarized in the paper. It is extended by concentration index application and visualizing of the results of social network analysis. The study of a model set exhibits that: 1) users can be successfully segmented on the base of their most mentioned topics, which is useful for a product placement and other commercial purposes; 2) distribution of number of posts by authors is highly uneven regardless to the topic of discussion; 3) users connected on-line typically live in the same geographical area; 4) users’ number of posts and centrality indices are correlated.

Suggested Citation

  • Kotyrlo , Elena, 2017. "Social network sites: What users post and to whom they address. Some approaches to the study," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 74-99.
  • Handle: RePEc:ris:apltrx:0325
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2017_47_074-099.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Goodreau, Steven M. & Handcock, Mark S. & Hunter, David R. & Butts, Carter T. & Morris, Martina, 2008. "A statnet Tutorial," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i09).
    3. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    4. Ivan Smirnov & Elizaveta Sivak & Yana Kozmina, 2016. "In Search of Lost Profiles: The Reliability of VKontakte Data and Its Importance for Educational Research," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 4, pages 106-122.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. De Masi, G. & Giovannetti, G. & Ricchiuti, G., 2013. "Network analysis to detect common strategies in Italian foreign direct investment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1202-1214.
    2. Fogel, Kathy & Jandik, Tomas & McCumber, William R., 2018. "CFO social capital and private debt," Journal of Corporate Finance, Elsevier, vol. 52(C), pages 28-52.
    3. Grinis, Inna, 2017. "The STEM requirements of "non-STEM" jobs: evidence from UK online vacancy postings and implications for skills & knowledge shortages," LSE Research Online Documents on Economics 85123, London School of Economics and Political Science, LSE Library.
    4. Hyuk-Soo Kwon & Jihong Lee & Sokbae Lee & Ryungha Oh, 2022. "Knowledge spillovers and patent citations: trends in geographic localization, 1976–2015," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 31(3), pages 123-147, April.
    5. Julia Bachtrögler & Christoph Hammer & Wolf Heinrich Reuter & Florian Schwendinger, 2019. "Guide to the galaxy of EU regional funds recipients: evidence from new data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(1), pages 103-150, February.
    6. Sándor Juhász, 2021. "Spinoffs and tie formation in cluster knowledge networks," Small Business Economics, Springer, vol. 56(4), pages 1385-1404, April.
    7. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    8. Fang, Ming & Francis, Bill & Hasan, Iftekhar & Wu, Qiang, 2022. "External social networks and earnings management," The British Accounting Review, Elsevier, vol. 54(2).
    9. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    10. Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
    11. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    12. Tao, Qizhi & Li, Haoyu & Wu, Qun & Zhang, Ting & Zhu, Yingjun, 2019. "The dark side of board network centrality: Evidence from merger performance," Journal of Business Research, Elsevier, vol. 104(C), pages 215-232.
    13. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    14. repec:dau:papers:123456789/7616 is not listed on IDEAS
    15. Jackie Krafft & Francesco Quatraro, 2011. "The Dynamics of Technological Knowledge: From Linearity to Recombination," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 7, Edward Elgar Publishing.
    16. Zhao, Shuying & Sun, Shaowei, 2023. "Identification of node centrality based on Laplacian energy of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    17. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    18. Daoud, Adel & Kohl, Sebastian, 2016. "How much do sociologists write about economic topics? Using big data to test some conventional views in economic sociology, 1890 to 2014," MPIfG Discussion Paper 16/7, Max Planck Institute for the Study of Societies.
    19. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.
    20. Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    21. Zamudio, César & Anokhin, Sergey & Kellermanns, Franz W., 2014. "Network analysis: A concise review and suggestions for family business research," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 63-71.

    More about this item

    Keywords

    text mining; social network analysis; social network sites; regression analysis; Gini coefficient.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • M39 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.