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On Two Existing Approaches to Statistical Analysis of Social Media Data

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  • Martina Patone
  • Li‐Chun Zhang

Abstract

Using social media data for statistical analysis of general population faces commonly two basic obstacles: firstly, social media data are collected for different objects than the population units of interest; secondly, the relevant measures are typically not available directly but need to be extracted by algorithms or machine learning techniques. In this paper, we examine and summarise two existing approaches to statistical analysis based on social media data, which can be discerned in the literature. In the first approach, analysis is applied to the social media data that are organised around the objects directly observed in the data; in the second one, a different analysis is applied to a constructed pseudo survey dataset, aimed to transform the observed social media data to a set of units from the target population. We elaborate systematically the relevant data quality frameworks, exemplify their applications and highlight some typical challenges associated with social media data.

Suggested Citation

  • Martina Patone & Li‐Chun Zhang, 2021. "On Two Existing Approaches to Statistical Analysis of Social Media Data," International Statistical Review, International Statistical Institute, vol. 89(1), pages 54-71, April.
    • Handle: RePEc:bla:istatr:v:89:y:2021:i:1:p:54-71
    DOI: 10.1111/insr.12404
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    1. Lilli Japec & Frauke Kreuter & Marcus Berg & Paul Biemer & Paul Decker & Cliff Lampe & Julia Lane & Cathy O’Neil & Abe Usher, "undated". "Big Data in Survey Research: AAPOR Task Force Report," Mathematica Policy Research Reports c57e7c039f6a4db982b26c6fe, Mathematica Policy Research.
    2. Dilek Yildiz & Jo Munson & Agnese Vitali & Ramine Tinati & Jennifer A. Holland, 2017. "Using Twitter data for demographic research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(46), pages 1477-1514.
    3. Li-Chun Zhang, 2019. "On valid descriptive inference from non-probability sample," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(2), pages 103-113, July.
    4. Skinner, Chris J. & Wakefield, Jon, 2017. "Introduction to the design and analysis of complex survey data," LSE Research Online Documents on Economics 76991, London School of Economics and Political Science, LSE Library.
    5. Daas, Piet J.H. & Puts, Marco J.H., 2014. "Social media sentiment and consumer confidence," Statistics Paper Series 5, European Central Bank.
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