  Associating geo-coordinates with the content of social media posts can
enhance many existing applications and services and enable a host of new ones.
Unfortunately, a majority of social media posts are not tagged with
geo-coordinates. Even when location data is available, it may be inaccurate,
very broad or sometimes fictitious. Contemporary location estimation approaches
based on analyzing the content of these posts can identify only broad areas
such as a city, which limits their usefulness. To address these shortcomings,
this paper proposes a methodology to narrowly estimate the geo-coordinates of
social media posts with high accuracy. The methodology relies solely on the
content of these posts and prior knowledge of the wide geographical region from
where the posts originate. An ensemble of language models, which are smoothed
over non-overlapping sub-regions of a wider region, lie at the heart of the
methodology. Experimental evaluation using a corpus of over half a million
tweets from New York City shows that the approach, on an average, estimates
locations of tweets to within just 2.15km of their actual positions.
