NDLI: You are where you tweet: a content-based approach to geo-locating twitter users

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You are where you tweet: a content-based approach to geo-locating twitter users

Content Provider	ACM Digital Library
Author	Lee, Kyumin Cheng, Zhiyuan Caverlee, James
Abstract	We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location.
Starting Page	759
Ending Page	768
Page Count	10
File Format	PDF
ISBN	9781450300995
DOI	10.1145/1871437.1871535
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spatial data mining Text mining Twitter Location-based estimation
Content Type	Text
Resource Type	Article

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