  For the purposes of computational dialectology or other geographically bound
text analysis tasks, texts must be annotated with their or their authors'
location. Many texts are locatable through explicit labels but most have no
explicit annotation of place. This paper describes a series of experiments to
determine how positionally annotated microblog posts can be used to learn
location-indicating words which then can be used to locate blog texts and their
authors. A Gaussian distribution is used to model the locational qualities of
words. We introduce the notion of placeness to describe how locational words
are.
  We find that modelling word distributions to account for several locations
and thus several Gaussian distributions per word, defining a filter which picks
out words with high placeness based on their local distributional context, and
aggregating locational information in a centroid for each text gives the most
useful results. The results are applied to data in the Swedish language.
