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Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal

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  • Iacus Stefano M.

    (Department of Economics, Management and Quantitative Methods, University of Milan, Via Conservatorio 7 - 20122, Milan, Italy.)

  • Porro Giuseppe

    (Department of Law, Economics and Culture, Univertity of Insubria, Via Sant’Abbondio, 12 - 22100, Como, Italy.)

  • Salini Silvia

    (Department of Economics, Management and Quantitative Methods, University of Milan, Via Conservatorio 7 - 20122, Milan, Italy.)

  • Siletti Elena

    (Department of Economics, Management and Quantitative Methods, University of Milan, Via Conservatorio 7 - 20122, Milan, Italy.)

Abstract

With the increase of social media usage, a huge new source of data has become available. Despite the enthusiasm linked to this revolution, one of the main outstanding criticisms in using these data is selection bias. Indeed, the reference population is unknown. Nevertheless, many studies show evidence that these data constitute a valuable source because they are more timely and possess higher space granularity. We propose to adjust statistics based on Twitter data by anchoring them to reliable official statistics through a weighted, space-time, small area estimation model. As a by-product, the proposed method also stabilizes the social media indicators, which is a welcome property required for official statistics. The method can be adapted anytime official statistics exists at the proper level of granularity and for which social media usage within the population is known. As an example, we adjust a subjective well-being indicator of “working conditions” in Italy, and combine it with relevant official statistics. The weights depend on broadband coverage and the Twitter rate at province level, while the analysis is performed at regional level. The resulting statistics are then compared with survey statistics on the “quality of job” at macro-economic regional level, showing evidence of similar paths.

Suggested Citation

  • Iacus Stefano M. & Porro Giuseppe & Salini Silvia & Siletti Elena, 2020. "Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal," Journal of Official Statistics, Sciendo, vol. 36(2), pages 315-338, June.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:2:p:315-338:n:6
    DOI: 10.2478/jos-2020-0017
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    2. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    3. Silvia Facchinetti & Elena Siletti, 2022. "Well-being Indicators: a Review and Comparison in the Context of Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(2), pages 523-547, January.
    4. Tiziana CARPI & Airo HINO & Stefano Maria IACUS & Giuseppe PORRO, 2022. "A Japanese Subjective Well-Being Indicator Based on Twitter Data [‘Collective Smile: Measuring Societal Happiness from Geolocated Images’]," Social Science Japan Journal, University of Tokyo and Oxford University Press, vol. 25(2), pages 273-296.
    5. Tiziana Carpi & Airo Hino & Stefano Maria Iacus & Giuseppe Porro, 2021. "Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study," Papers 2101.07695, arXiv.org.
    6. Federica Cugnata & Silvia Salini & Elena Siletti, 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach," IJERPH, MDPI, vol. 18(15), pages 1-10, July.

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