<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Department of Economics, Management and Quantitative Methods

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Published Online: Jun 15, 2020

Page range: 315 - 338

Received: Mar 01, 2019

Accepted: Jan 01, 2020

DOI: https://doi.org/10.2478/jos-2020-0017

Keywords
Well-being, big data, sentiment analysis, small area estimation, weighting

© 2020 Stefano M. Iacus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Jun 15, 2020

Page range: 315 - 338

Received: Mar 01, 2019

Accepted: Jan 01, 2020

DOI: https://doi.org/10.2478/jos-2020-0017

KeywordsWell-being, big data, sentiment analysis, small area estimation, weighting

© 2020 Stefano M. Iacus et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Well-being, big data, sentiment analysis, small area estimation, weighting