  Metrics derived from Twitter and other social media---often referred to as
altmetrics---are increasingly used to estimate the broader social impacts of
scholarship. Such efforts, however, may produce highly misleading results, as
the entities that participate in conversations about science on these platforms
are largely unknown. For instance, if altmetric activities are generated mainly
by scientists, does it really capture broader social impacts of science? Here
we present a systematic approach to identifying and analyzing scientists on
Twitter. Our method can identify scientists across many disciplines, without
relying on external bibliographic data, and be easily adapted to identify other
stakeholder groups in science. We investigate the demographics, sharing
behaviors, and interconnectivity of the identified scientists. We find that
Twitter has been employed by scholars across the disciplinary spectrum, with an
over-representation of social and computer and information scientists;
under-representation of mathematical, physical, and life scientists; and a
better representation of women compared to scholarly publishing. Analysis of
the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a
small fraction of shared URLs are science-related. We find an assortative
mixing with respect to disciplines in the networks between scientists,
suggesting the maintenance of disciplinary walls in social media. Our work
contributes to the literature both methodologically and conceptually---we
provide new methods for disambiguating and identifying particular actors on
social media and describing the behaviors of scientists, thus providing
foundational information for the construction and use of indicators on the
basis of social media metrics.
