Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | SIAM International Conference on Data Mining, SDM 2019 |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 64-72 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781611975673 |
| State | Published - Jan 1 2019 |
| Event | 19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada Duration: May 2 2019 → May 4 2019 |
Publication series
| Name | SIAM International Conference on Data Mining, SDM 2019 |
|---|
Conference
| Conference | 19th SIAM International Conference on Data Mining, SDM 2019 |
|---|---|
| Country | Canada |
| City | Calgary |
| Period | 5/2/19 → 5/4/19 |
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ASJC Scopus subject areas
- Software
Cite this
GeoAttn : Localization of social media messages via attentional memory network. / Li, Sha; Zhang, Chao; Lei, Dongming; Li, Ji; Han, Jiawei.
SIAM International Conference on Data Mining, SDM 2019. Society for Industrial and Applied Mathematics Publications, 2019. p. 64-72 (SIAM International Conference on Data Mining, SDM 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - GeoAttn
T2 - Localization of social media messages via attentional memory network
AU - Li, Sha
AU - Zhang, Chao
AU - Lei, Dongming
AU - Li, Ji
AU - Han, Jiawei
PY - 2019/1/1
Y1 - 2019/1/1
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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85066086658&partnerID=8YFLogxK
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
ER -