Implementation of a Realtime Event-location Analyzer
Junyeob Yim, Bumsuk Lee, Byung-Yeon Hwang
2015
Abstract
A Social Networking Service (SNS) is a web-based platform that helps to build or to keep relationships among people. The SNS platforms in early stage including Friendster and MySpace were implemented for the desktop and laptop users. As more people access wireless internet using their mobile phones, SNS platforms can also have some important features such as “real-time access” and “location information”. These two features make it possible to let people share their activities, interests, and observations in real-time at any places. Recently, most of SNS platforms including Twitter, Facebook, and Yelp use the location information of users. Therefore, if we consider a SNS user as a sensor that reports its observations at a specific location, it would be possible to detect events by analyzing their social contents. There are already numbers of research on this topic have been published or still ongoing. Twitter has been widely used for conducting the research because it has important three features which are required to detect an event: time, location, and content. However, the most approaches struggle with detecting the location which is related to an event correctly. In this paper, we introduce a system that detects an event with its location in real-time based on increment of tweets that mention a specific location frequently. The result of performance evaluation shows that the proposed system detects an event in real-time. We also improved the system performance by reducing some noises from our system.
References
- Barbosa, L., Feng. J., 2010. Robust Sentiment Detection on Twitter from Biased and Noisy Data. In Proc. of 23rd Int'l Conf. on Computational Linguistics. pp. 36-44.
- Lee, B., 2012. A Temporal Analysis of Posting Behavior in Social Media Streams. In Proc. of ICWSM'12 Workshop on Social Media Visualization.
- Hong, C., Kim, H., 2011. Effective Feature Extraction for Tweets Classification. In Proc. of Korea Computer Congress. pp. 229-232.
- Lee, B., Hwang, B. Y., 2012. A Study of the Correlation between the Spatial Attributes on Twitter. In Proc. of ICDE'12 Workshop on Spatio-Temporal Data Integration and Retrieval.
- Lee, B., Yoon, J., Kim, S., and Hwang, B. Y., 2012. Detecting Social Signals of Flu Symptoms. In Proc. of COLLABORATECOM'12. pp. 544-545.
- Lee, J., Bengio, S., Kim, S., Lebanon, G., and Singer, Y., 2014. Local Collaborative Ranking. In Proc of 23rd Int'l Conf. on WWW Conference Committee. pp 85-95.
- Sakaki, T., Okzaki, M., and Matsuo, Y., 2010. Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In Proc. of the 19th International Conference on WWW. pp. 851-860.
- Li, R., Lei, K. H., Khadiwala, R., and Chang, K. C. C., 2012. TEDAS: a Twitter based Event Detection and Analysis System. In Proc of the 28th International Conference on Data Engineering. pp. 1273-1276.
Paper Citation
in Harvard Style
Yim J., Lee B. and Hwang B. (2015). Implementation of a Realtime Event-location Analyzer . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 349-352. DOI: 10.5220/0005193903490352
in Bibtex Style
@conference{icaart15,
author={Junyeob Yim and Bumsuk Lee and Byung-Yeon Hwang},
title={Implementation of a Realtime Event-location Analyzer},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={349-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005193903490352},
isbn={978-989-758-074-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Implementation of a Realtime Event-location Analyzer
SN - 978-989-758-074-1
AU - Yim J.
AU - Lee B.
AU - Hwang B.
PY - 2015
SP - 349
EP - 352
DO - 10.5220/0005193903490352