%0 Conference Paper %1 6113226 %A Chandra, S. %A Khan, L. %A Muhaya, F. B. %B Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on %D 2011 %K location m1 twitter %P 838-843 %R 10.1109/PASSAT/SocialCom.2011.120 %T Estimating Twitter User Location Using Social Interactions--A Content Based Approach %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6113226&tag=1 %X 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.

@inproceedings{6113226, abstract = {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.}, added-at = {2016-06-17T11:57:50.000+0200}, author = {Chandra, S. and Khan, L. and Muhaya, F. B.}, biburl = {https://www.bibsonomy.org/bibtex/2cf3631784176bce0afd4955f4060b4de/asmelash}, booktitle = {Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on}, description = {IEEE Xplore Abstract - Estimating Twitter User Location Using Social Interactions--A Content Based Approach}, doi = {10.1109/PASSAT/SocialCom.2011.120}, interhash = {013daa546d5769b1b66ddeb4a446737b}, intrahash = {cf3631784176bce0afd4955f4060b4de}, keywords = {location m1 twitter}, month = oct, pages = {838-843}, timestamp = {2016-06-17T11:57:50.000+0200}, title = {Estimating Twitter User Location Using Social Interactions--A Content Based Approach}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6113226&tag=1}, year = 2011 }