TY - GEN
T1 - Location Prediction with Communities in User Ego-Net in Social Media
AU - Wagenseller, Paul
AU - Avram, Adrian
AU - Jiang, Eric
AU - Wang, Feng
AU - Zhao, Yunpeng
N1 - Funding Information:
ACKNOWLEDGMENT This project is supported by NSF grant ATD #1737861.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Social media embed rich but noisy signals of physical locations of their users. Accurately inferring a user's location can significantly improve the user's experience on the social media and enable the development of new location-based applications. This paper proposes a novel community-based approach for predicting the location of a user by using communities in the egonet of the user. We further propose both geographical proximity and structural proximity metrics to profile communities in the ego-net of a user, and then evaluate the effectiveness of each individual metric on real social media data. We discover that geographical proximity metrics, such as average/median haversine distance and community closeness, are strong indicators of a good community for geotagging. In addition, structural proximity metric conductance performs comparable to geographical proximity metrics while triangle participation ratio and internal density are weak location indicators. To the best of our knowledge, this is the first effort to infer the physical location of a user from the perspective of latent communities in the user's ego-net.
AB - Social media embed rich but noisy signals of physical locations of their users. Accurately inferring a user's location can significantly improve the user's experience on the social media and enable the development of new location-based applications. This paper proposes a novel community-based approach for predicting the location of a user by using communities in the egonet of the user. We further propose both geographical proximity and structural proximity metrics to profile communities in the ego-net of a user, and then evaluate the effectiveness of each individual metric on real social media data. We discover that geographical proximity metrics, such as average/median haversine distance and community closeness, are strong indicators of a good community for geotagging. In addition, structural proximity metric conductance performs comparable to geographical proximity metrics while triangle participation ratio and internal density are weak location indicators. To the best of our knowledge, this is the first effort to infer the physical location of a user from the perspective of latent communities in the user's ego-net.
KW - Ego-net
KW - Twitter
KW - community detection
KW - geographical proximity
KW - structural proximity
UR - http://www.scopus.com/inward/record.url?scp=85070191006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070191006&partnerID=8YFLogxK
U2 - 10.1109/ICC.2019.8761695
DO - 10.1109/ICC.2019.8761695
M3 - Conference contribution
AN - SCOPUS:85070191006
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -