TY - GEN
T1 - Joint localization of events and sources in social networks
AU - Giridhar, Prasanna
AU - Wang, Shiguang
AU - Abdelzaher, Tarek F.
AU - George, Jemin
AU - Kaplan, Lance
AU - Ganti, Raghu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/22
Y1 - 2015/7/22
N2 - Recent sensor network literature investigated the use of social networks as sensor networks, and formulated a physical event localization problem from social network data. This paper improves on the above results by formulating a joint localization problem of events and sources, leveraging the fact that sources on social networks often have a location affinity: They tend to comment more on events in their locations of interest. While social networks, such as Twitter, do not offer source location information for the majority of sources, we show that our algorithms for jointly inferring source and event location significantly improve localization quality by mutually enhancing location estimation of both events and sources. We evaluate the performance of our algorithm both in simulation and using Twitter data about current events. The results show that joint inference of source and event location allows us to localize many more of the events identified in real-world datasets.
AB - Recent sensor network literature investigated the use of social networks as sensor networks, and formulated a physical event localization problem from social network data. This paper improves on the above results by formulating a joint localization problem of events and sources, leveraging the fact that sources on social networks often have a location affinity: They tend to comment more on events in their locations of interest. While social networks, such as Twitter, do not offer source location information for the majority of sources, we show that our algorithms for jointly inferring source and event location significantly improve localization quality by mutually enhancing location estimation of both events and sources. We evaluate the performance of our algorithm both in simulation and using Twitter data about current events. The results show that joint inference of source and event location allows us to localize many more of the events identified in real-world datasets.
UR - http://www.scopus.com/inward/record.url?scp=84945966513&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945966513&partnerID=8YFLogxK
U2 - 10.1109/DCOSS.2015.14
DO - 10.1109/DCOSS.2015.14
M3 - Conference contribution
AN - SCOPUS:84945966513
T3 - Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2015
SP - 179
EP - 188
BT - Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2015
Y2 - 10 June 2015 through 12 June 2015
ER -