TY - GEN
T1 - GeoAttn
T2 - 19th SIAM International Conference on Data Mining, SDM 2019
AU - Li, Sha
AU - Zhang, Chao
AU - Lei, Dongming
AU - Li, Ji
AU - Han, Jiawei
N1 - Funding Information:
Research was sponsored in part by U.S. Army Research Lab. under Cooperative Agreement No. W911NF-09-2-0053 (NSCTA), DARPA under Agreement No. W911NF-17-C-0099, National Science Foundation IIS 16-18481, IIS 17-04532, and IIS-17-41317, DTRA HDTRA11810026, and grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov). Any opinions, findings, and conclusions or recommendations expressed in this document are those of the author(s) and should not be interpreted as the views of any U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
Funding Information:
Acknowledgements. Research was sponsored in part by
Publisher Copyright:
Copyright © 2019 by SIAM.
PY - 2019
Y1 - 2019
N2 - Recent studies have demonstrated inspiring success in leveraging geo-tagged social media data for applications such as event detection, location recommendation and mobile healthcare. However, in most real-life social media streams, only a small percentage of data have explicit geo-location metadata, which hinders the power of social media from being fully unleashed. We study the problem of inferring geo-locations from social media messages. While a number of text-based geo-locating techniques have been proposed, they either fall short of automatically identifying indicative keywords from noisy social media posts or do not integrate rich prior knowledge of geological regions. We propose an attentive memory network called GeoAttn for localization of social media messages. To capture indicative keywords for location inference, GeoAttn consists of an attentive message encoder, which selectively focuses on location-indicative terms to derive a discriminative message representation. The message embedding is then fed into a memory network, which selectively attends to relevant Points-of-Interest (POIs) for location prediction. The message encoder and key-value memory network are jointly trained in an end-to-end manner. The attention mechanisms in GeoAttn not only alleviate noisy information for higher prediction accuracy, but also provide interpretable attention scores that rationalize the predictions. Our experiments on a million-scale geo-tagged tweet dataset show that GeoAttn outperforms previous state-of-the-art location prediction methods by 15.5% in mean error distance, and is capable of locating over half of the tweets within 5km.
AB - Recent studies have demonstrated inspiring success in leveraging geo-tagged social media data for applications such as event detection, location recommendation and mobile healthcare. However, in most real-life social media streams, only a small percentage of data have explicit geo-location metadata, which hinders the power of social media from being fully unleashed. We study the problem of inferring geo-locations from social media messages. While a number of text-based geo-locating techniques have been proposed, they either fall short of automatically identifying indicative keywords from noisy social media posts or do not integrate rich prior knowledge of geological regions. We propose an attentive memory network called GeoAttn for localization of social media messages. To capture indicative keywords for location inference, GeoAttn consists of an attentive message encoder, which selectively focuses on location-indicative terms to derive a discriminative message representation. The message embedding is then fed into a memory network, which selectively attends to relevant Points-of-Interest (POIs) for location prediction. The message encoder and key-value memory network are jointly trained in an end-to-end manner. The attention mechanisms in GeoAttn not only alleviate noisy information for higher prediction accuracy, but also provide interpretable attention scores that rationalize the predictions. Our experiments on a million-scale geo-tagged tweet dataset show that GeoAttn outperforms previous state-of-the-art location prediction methods by 15.5% in mean error distance, and is capable of locating over half of the tweets within 5km.
UR - http://www.scopus.com/inward/record.url?scp=85066086658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066086658&partnerID=8YFLogxK
U2 - 10.1137/1.9781611975673.8
DO - 10.1137/1.9781611975673.8
M3 - Conference contribution
AN - SCOPUS:85066086658
T3 - SIAM International Conference on Data Mining, SDM 2019
SP - 64
EP - 72
BT - SIAM International Conference on Data Mining, SDM 2019
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 2 May 2019 through 4 May 2019
ER -