TY - GEN
T1 - Sense and Focus
T2 - 16th International Conference on Web Information Systems Engineering, WISE 2015
AU - Zhang, Yihong
AU - Szabo, Claudia
AU - Sheng, Quan Z.
PY - 2015
Y1 - 2015
N2 - Twitter users post observations about their immediate environment as a part of the 500 million tweets posted everyday. As such, Twitter can become the source for invaluable information about objects, locations, and events, which can be analyzed and monitored in real time, not only to understand what is happening in the world, but also an event’s exact location. However, Twitter data is noisy as sensory values, and information such as the location of a tweet may not be available, e.g., only 0.9% of tweets have GPS data. Due to the lack of accurate and fine-grained location information, existing Twitter event monitoring systems focus on city-level or coarser location identification, which cannot provide details for local events. In this paper, we propose SNAF (Sense and Focus), an event monitoring system for Twitter data that emphasizes local events. We increase the availability of the location information significantly by finding locations in tweet messages and users’ past tweets.We apply data cleaning techniques in our system, and with extensive experiments, we show that our method can improve the accuracy of location inference by 5% to 20% across different error ranges. We also show that our prototype implementation of SNAF can identify critical local events in real time, in many cases earlier than news reports.
AB - Twitter users post observations about their immediate environment as a part of the 500 million tweets posted everyday. As such, Twitter can become the source for invaluable information about objects, locations, and events, which can be analyzed and monitored in real time, not only to understand what is happening in the world, but also an event’s exact location. However, Twitter data is noisy as sensory values, and information such as the location of a tweet may not be available, e.g., only 0.9% of tweets have GPS data. Due to the lack of accurate and fine-grained location information, existing Twitter event monitoring systems focus on city-level or coarser location identification, which cannot provide details for local events. In this paper, we propose SNAF (Sense and Focus), an event monitoring system for Twitter data that emphasizes local events. We increase the availability of the location information significantly by finding locations in tweet messages and users’ past tweets.We apply data cleaning techniques in our system, and with extensive experiments, we show that our method can improve the accuracy of location inference by 5% to 20% across different error ranges. We also show that our prototype implementation of SNAF can identify critical local events in real time, in many cases earlier than news reports.
KW - Event detection
KW - Location inference
KW - Microblog content classification
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84949968221&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26190-4_31
DO - 10.1007/978-3-319-26190-4_31
M3 - Conference proceeding contribution
AN - SCOPUS:84949968221
SN - 9783319261898
VL - 9418
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 463
EP - 477
BT - Web Information Systems Engineering – WISE 2015
A2 - Wang, Jianyong
A2 - Cellary, Wojciech
A2 - Wang, Dingding
A2 - Wang, Hua
A2 - Chen, Shu-Ching
A2 - Li, Tao
A2 - Zhang, Yanchun
PB - Springer, Springer Nature
CY - Cham
Y2 - 1 November 2015 through 3 November 2015
ER -