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

Stefano Iacus (), Giuseppe Porro, Silvia Salini () and Elena Siletti

Journal of Official Statistics, 2020, vol. 36, issue 2, 315-338

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.

Keywords: Well-being; big data; sentiment analysis; small area estimation; weighting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Journal Article: Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:36:y:2020:i:2:p:315-338:n:6

DOI: 10.2478/jos-2020-0017

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