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  1. Proceedings of the 3rd international workshop on Search and mining user-generated contents (SMUC '11)
  2. "I'm eating a sandwich in Glasgow": modeling locations with tweets
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The challenge of understanding the flow of sentiments in social media documents
Trend-based and reputation-versed personalized news network
Improved answer ranking in social question-answering portals
Analysis of communities in social media
Predicting age and gender in online social networks
"I'm eating a sandwich in Glasgow": modeling locations with tweets
A comparative evaluation of personality estimation algorithms for the twin recommender system
Detection of near-duplicate user generated contents: the SMS spam collection
Characterizing Wikipedia pages using edit network motif profiles
Mining tweets for tag recommendation on social media
Mining tag similarity in folksonomies
ThemeCrowds: multiresolution summaries of twitter usage
On the generation of rich content metadata from social media

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"I'm eating a sandwich in Glasgow": modeling locations with tweets

Content Provider ACM Digital Library
Author Murdock, Vanessa O'Hare, Neil Kinsella, Sheila
Abstract Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a particular location. We leverage this data to build models of locations to improve our understanding of a user's geographic context. Understanding the user's geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level. In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for predicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level.
Starting Page 61
Ending Page 68
Page Count 8
File Format PDF
ISBN 9781450309493
DOI 10.1145/2065023.2065039
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-10-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Geographic targeting Twitter Language models
Content Type Text
Resource Type Article
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