TY - GEN
T1 - Geosocial Location Classification
T2 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2020
AU - Kravi, Elad
AU - Kanza, Yaron
AU - Kimelfeld, Benny
AU - Reichart, Roi
N1 - Publisher Copyright: © 2020 Owner/Author.
PY - 2020/11/3
Y1 - 2020/11/3
N2 - Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each message is first classified, and then the location associated with the message set is inferred from the separate message labels; and (b) a joint approach where the messages are simultaneously processed to yield the desired location type. We tested the two approaches over a dataset of geotagged tweets. Our results demonstrate the superiority of the joint approach.
AB - Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each message is first classified, and then the location associated with the message set is inferred from the separate message labels; and (b) a joint approach where the messages are simultaneously processed to yield the desired location type. We tested the two approaches over a dataset of geotagged tweets. Our results demonstrate the superiority of the joint approach.
KW - Geosocial
KW - ML
KW - classification
KW - location type
KW - social media
UR - https://www.scopus.com/pages/publications/85097305916
U2 - 10.1145/3397536.3422214
DO - 10.1145/3397536.3422214
M3 - Conference contribution
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 167
EP - 170
BT - Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Trajcevski, Goce
A2 - Huang, Yan
A2 - Newsam, Shawn
A2 - Xiong, Li
Y2 - 3 November 2020 through 6 November 2020
ER -