  Microsoft Academic is a free academic search engine and citation index that
is similar to Google Scholar but can be automatically queried. Its data is
potentially useful for bibliometric analysis if it is possible to search
effectively for individual journal articles. This article compares different
methods to find journal articles in its index by searching for a combination of
title, authors, publication year and journal name and uses the results for the
widest published correlation analysis of Microsoft Academic citation counts for
journal articles so far. Based on 126,312 articles from 323 Scopus subfields in
2012, the optimal strategy to find articles with DOIs is to search for them by
title and filter out those with incorrect DOIs. This finds 90% of journal
articles. For articles without DOIs, the optimal strategy is to search for them
by title and then filter out matches with dissimilar metadata. This finds 89%
of journal articles, with an additional 1% incorrect matches. The remaining
articles seem to be mainly not indexed by Microsoft Academic or indexed with a
different language version of their title. From the matches, Scopus citation
counts and Microsoft Academic counts have an average Spearman correlation of
0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic
citation counts are almost universally equivalent to Scopus citation counts for
articles that are not recent but there are national biases in the results.
