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Abstract
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Data quality issues regarding social media data have been highlighted as one of the grand
challenges for the development of altmetrics (Haustein, 2016; Bar-Ilan & Halevi, 2017;
Chamberlain, 2013; Peters et al., 2014; Zahedi, Fenner, & Costas, 2014, 2015). Production of
transparent and reliable social media indicators is very critical for the reliability and validity of
these indicators and for the future development of more advanced social media studies of science
(Costas, 2017). Development and application of social media metrics is dependent on the
characteristics and quality of the underlying data. Altmetric data aggregators offer access to data
and metrics related with the online activity and social media interactions between social media
users and scholarly objects. Methodological choices in the tracking, collecting, and reporting of
altmetric data could influence the metrics provided by different altmetric data aggregators.
Understanding the extent to which social media metrics from similar data sources are correlated
across different altmetric data aggregators and understanding the underlying reasons of
inconsistencies in their metrics is central for the proper development of social media metrics
based on these data. This paper studies how consistent the different aggregators are in terms of
the social media metrics provided by them and discusses the extent to which the strategies and
the methodological approaches in the data aggregation and reporting metrics adapted by altmetric
data aggregators introduce challenges for interpreting the provided metrics. The final aim of this
paper is to create awareness of the effects of these differences in the conceptual meaning and
interpretation of social media metrics.
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