Cart (Loading....) | Create Account
Close category search window
 

Estimating Twitter User Location Using Social Interactions--A Content Based Approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chandra, S. ; Dept. of Comput. Sci., Univ. of Texas at Dallas, Dallas, TX, USA ; Khan, L. ; Muhaya, F.B.

Microblogging services such as Twitter allow users to interact with each other by forming a social network. The interaction between users in a social network group forms a dialogue or discussion. A typical dialogue between users involves a set of topics. We make the assumption that this set of topics remains constant throughout the conversation. Using this model of social interaction between users in the Twitter social network, along with content-derived location information, we employ a probabilistic framework to estimate the city-level location of a Twitter user, based on the content of the tweets in their dialogues, using reply-tweet messages. We estimate the city-level user location based purely on the content of the tweets, which may include reply-tweet information, without the use of any external information, such as a gazetteer, IP information etc. The current framework for estimating user location does not consider the underlying social interaction, i.e. the structure of interactions between the users. In this paper, we calculate a baseline probability estimate of the distribution of words used by a user. This distribution is formed by using the fact that terms used in the tweets of a certain discussion may be related to the location information of the user initiating the discussion. We also estimate the top K probable cities for a given user and measure the accuracy. We find that our baseline estimation yields an accuracy higher that the 10% accuracy of the current state of the art estimation.

Published in:

Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on

Date of Conference:

9-11 Oct. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.