  The movements of ideas and content between locations and languages are
unquestionably crucial concerns to researchers of the information age, and
Twitter has emerged as a central, global platform on which hundreds of millions
of people share knowledge and information. A variety of research has attempted
to harvest locational and linguistic metadata from tweets in order to
understand important questions related to the 300 million tweets that flow
through the platform each day. However, much of this work is carried out with
only limited understandings of how best to work with the spatial and linguistic
contexts in which the information was produced. Furthermore, standard,
well-accepted practices have yet to emerge. As such, this paper studies the
reliability of key methods used to determine language and location of content
in Twitter. It compares three automated language identification packages to
Twitter's user interface language setting and to a human coding of languages in
order to identify common sources of disagreement. The paper also demonstrates
that in many cases user-entered profile locations differ from the physical
locations users are actually tweeting from. As such, these open-ended,
user-generated, profile locations cannot be used as useful proxies for the
physical locations from which information is published to Twitter.
