TY - GEN
T1 - Location Prediction in Social Networks
AU - Liu, Rong
AU - Cong, Guanglin
AU - Zheng, Bolong
AU - Zheng, Kai
AU - Su, Han
PY - 2018/1/1
Y1 - 2018/1/1
N2 - User locations in social networks are needed in many applications which utilize location information to recommend local news and places of interest to users, as well as detect and alert emergencies around users. However, considering individual privacy, only a small portion users share their location on social networks. Thus, to predict the fine-grained locations of user tweets, we present a joint model containing three sub models: content-based model, social relationship based model and behavior habit based model. In the content-based model, we filter out those location-independent tweets and use deep learning algorithm to mine the relationship between semantics and locations. User trajectory similarity measure is used to build a social graph for users, and historical check-ins is used to provide users’ daily activity habits. We conduct experiments using tweets collected from Shanghai during one year. The result shows that our joint model perform well, especially the content-based model. We find that our approach improves accuracy compared to the state-of-the-art location prediction algorithm.
AB - User locations in social networks are needed in many applications which utilize location information to recommend local news and places of interest to users, as well as detect and alert emergencies around users. However, considering individual privacy, only a small portion users share their location on social networks. Thus, to predict the fine-grained locations of user tweets, we present a joint model containing three sub models: content-based model, social relationship based model and behavior habit based model. In the content-based model, we filter out those location-independent tweets and use deep learning algorithm to mine the relationship between semantics and locations. User trajectory similarity measure is used to build a social graph for users, and historical check-ins is used to provide users’ daily activity habits. We conduct experiments using tweets collected from Shanghai during one year. The result shows that our joint model perform well, especially the content-based model. We find that our approach improves accuracy compared to the state-of-the-art location prediction algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85051120763&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-96893-3_12
DO - 10.1007/978-3-319-96893-3_12
M3 - Article in proceeding
AN - SCOPUS:85051120763
SN - 9783319968926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 165
BT - Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
PB - Springer
T2 - 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Y2 - 23 July 2018 through 25 July 2018
ER -