TY - GEN
T1 - Phillies tweeting from philly? Predicting twitter user locations with spatial word usage
AU - Chang, Hau Wen
AU - Lee, Dongwon
AU - Eltaher, Mohammed
AU - Lee, Jeongkyu
PY - 2012
Y1 - 2012
N2 - We study the problem of predicting home locations of Twitter users using contents of their tweet messages. Using three probability models for locations, we compare both the Gaussian Mixture Model (GMM) and the Maximum Likelihood Estimation (MLE). In addition, we propose two novel unsupervised methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data from tweet messages. In the experiments, our unsupervised approach improves the baselines significantly and shows comparable results with the supervised state-of-the-art method. For 5,113 Twitter users in the test set, on average, our approach with only 250 selected local words or less is able to predict their home locations (within 100 miles) with the accuracy of 0.499, or has 509.3 miles of average error distance at best.
AB - We study the problem of predicting home locations of Twitter users using contents of their tweet messages. Using three probability models for locations, we compare both the Gaussian Mixture Model (GMM) and the Maximum Likelihood Estimation (MLE). In addition, we propose two novel unsupervised methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data from tweet messages. In the experiments, our unsupervised approach improves the baselines significantly and shows comparable results with the supervised state-of-the-art method. For 5,113 Twitter users in the test set, on average, our approach with only 250 selected local words or less is able to predict their home locations (within 100 miles) with the accuracy of 0.499, or has 509.3 miles of average error distance at best.
UR - http://www.scopus.com/inward/record.url?scp=84874276761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874276761&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2012.29
DO - 10.1109/ASONAM.2012.29
M3 - Conference contribution
AN - SCOPUS:84874276761
SN - 9780769547992
T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
SP - 111
EP - 118
BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
T2 - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Y2 - 26 August 2012 through 29 August 2012
ER -