Social media represent an excellent opportunity for the construction of timely socio-economic indicators. Despite the many advantages of investigating social media for this purpose, however, there are also relevant statistical and quality issues. Data quality is an especially critical topic. Depending on the characteristics of the social media a researcher is using, the problems that arise related to errors are different. Thus, no one unique quality evaluation framework is suitable. In this paper, the quality of social media data is discussed considering Twitter as the reference social media. An original quality framework for Twitter data is introduced. A reformulation of the traditional quality dimensions is proposed, and the new quality aspects are discussed. The main sources of errors are identified, and examples are provided to show the process of finding evidence of these errors. The conclusion affirms the importance of using a mixed methods approach, which involves incorporating both qualitative and quantitative evaluations to assess data quality. A collection of good practices and proposed indicators for quality evaluation is provided.

Salvatore, C., Biffignandi, S., Bianchi, A. (2021). Social Media and Twitter Data Quality for New Social Indicators. SOCIAL INDICATORS RESEARCH, 156(2-3), 601-630 [10.1007/s11205-020-02296-w].

Social Media and Twitter Data Quality for New Social Indicators

Salvatore C.;
2021

Abstract

Social media represent an excellent opportunity for the construction of timely socio-economic indicators. Despite the many advantages of investigating social media for this purpose, however, there are also relevant statistical and quality issues. Data quality is an especially critical topic. Depending on the characteristics of the social media a researcher is using, the problems that arise related to errors are different. Thus, no one unique quality evaluation framework is suitable. In this paper, the quality of social media data is discussed considering Twitter as the reference social media. An original quality framework for Twitter data is introduced. A reformulation of the traditional quality dimensions is proposed, and the new quality aspects are discussed. The main sources of errors are identified, and examples are provided to show the process of finding evidence of these errors. The conclusion affirms the importance of using a mixed methods approach, which involves incorporating both qualitative and quantitative evaluations to assess data quality. A collection of good practices and proposed indicators for quality evaluation is provided.
Articolo in rivista - Articolo scientifico
Big Data; Error; Quality; Twitter;
English
19-feb-2020
2021
156
2-3
601
630
none
Salvatore, C., Biffignandi, S., Bianchi, A. (2021). Social Media and Twitter Data Quality for New Social Indicators. SOCIAL INDICATORS RESEARCH, 156(2-3), 601-630 [10.1007/s11205-020-02296-w].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/277844
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