Why name ambiguity resolution matters for scholarly big data research

This paper illustrates how data pre-processing choices about author name disambiguation can affect research findings about scholarly networks and hypotheses about underlying social mechanisms. We have analyzed three big scholarly datasets that were disambiguated algorithmically and via two common initial-based disambiguation methods; namely first-initial and all-initials disambiguation. The comparison of resulting bibliometric and network properties revealed that initial-disambiguation bears the prevalent risks of incorrectly merging author identities, underestimating the number of unique authors and inflating the average productivity and number of collaborators per author. The gaps between outcomes of name ambiguity resolution methods range from -4.23% to -87.36% per dataset for the number of unique authors, from 3.75% to 691.20% for average productivity, and from 5.06% to 285.28% for degree centrality for initial based methods compared to algorithmic disambiguation. This calls for special attention to data pre-processing choices in scholarly big data research.