TY - GEN
T1 - On localizing urban events with Instagram
AU - Giridhar, Prasanna
AU - Wang, Shiguang
AU - Abdelzaher, Tarek
AU - Ganti, Raghu
AU - Kaplan, Lance
AU - George, Jemin
N1 - Research reported in this paper was sponsored by the Army Research Laboratory and NSF, and was accomplished under Cooperative Agreement W911NF-09-2-0053, and NSF grants CNS 16-18627 and CNS 13-29886. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, NSF, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - This paper develops an algorithm that exploits picture-oriented social networks to localize urban events. We choose picture-oriented networks because taking a picture requires physical proximity, thereby revealing the location of the photographed event. Furthermore, most modern cell phones are equipped with GPS, making picture location, and time metadata commonly available. We consider Instagram as the social network of choice and limit ourselves to urban events (noting that the majority of the world population lives in cities). The paper introduces a new adaptive localization algorithm that does not require the user to specify manually tunable parameters. We evaluate the performance of our algorithm for various real-world datasets, comparing it against a few baseline methods. The results show that our method achieves the best recall, the fewest false positives, and the lowest average error in localizing urban events.
AB - This paper develops an algorithm that exploits picture-oriented social networks to localize urban events. We choose picture-oriented networks because taking a picture requires physical proximity, thereby revealing the location of the photographed event. Furthermore, most modern cell phones are equipped with GPS, making picture location, and time metadata commonly available. We consider Instagram as the social network of choice and limit ourselves to urban events (noting that the majority of the world population lives in cities). The paper introduces a new adaptive localization algorithm that does not require the user to specify manually tunable parameters. We evaluate the performance of our algorithm for various real-world datasets, comparing it against a few baseline methods. The results show that our method achieves the best recall, the fewest false positives, and the lowest average error in localizing urban events.
UR - https://www.scopus.com/pages/publications/85022179454
UR - https://www.scopus.com/pages/publications/85022179454#tab=citedBy
U2 - 10.1109/INFOCOM.2017.8057006
DO - 10.1109/INFOCOM.2017.8057006
M3 - Conference contribution
AN - SCOPUS:85022179454
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2017 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Conference on Computer Communications, INFOCOM 2017
Y2 - 1 May 2017 through 4 May 2017
ER -