Abstract
Among the existing social platforms, Twitter plays a more and more important role in social sensing due to its real-time nature. In particular, it reports various types of events occurred in the real world, which provides us the possibility of tracking entities of interest (e.g., celebrities, organizations) in real time via event analysis. Hence, this paper presents TwiTracker, a system for obtaining the timelines of entities on Twitter. The system uses Twitter API and keyword search to collect tweets containing the entities of interest, and combines event detection and extraction together to extract elements including activities, time, location and participants. Online incremental clustering is further applied to fuse extraction results from different tweets to remove redundant information and enhance accuracy. Echarts is used to visualize the dynamic trajectory of each entity under tracking. For evaluation, we take Golden State Warriors, a famous NBA team, as well as the stars in the team as the experimental objects to compute their timelines, and compare the experimental results with the ground truth data hunted from the Internet, which demonstrates TwiTracker is effective for tracking entities and can provide information that is not covered by newswires.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, R., Lei, K.H., Khadiwala, R., et al.: TEDAS: a Twitter-based event detection and analysis system. In: IEEE International Conference on Data Engineering (ICDE), 1–5 April 2012
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: The International Conference on World Wide Web (WWW), Raleigh, North Carolina, USA (2010)
Vieweg, S., Hughes, A. L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: The SIGHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA (2010)
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011)
Marcus, A., Bernstein, M.S., Badar, O., et al.: TwitInfo: aggregating and visualizing microblogs for event exploration. In: The SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada, ACM (2011). https://doi.org/10.1145/1978942.1978975
Popescu, A.-M., Pennacchiotti, M., Paranjpe, D.: Extracting events and event descriptions from Twitter. In: The International Conference on World Wide Web (WWW), Hyderabad, India. ACM (2011). https://doi.org/10.1145/1963192.1963246
Li, J., Cardie, C.: Timeline generation: tracking individuals on Twitter. In: The International Conference on World Wide Web (WWW), Seoul, Korea. ACM (2014). https://doi.org/10.1145/2566486.2567969
Echarts Homepage. http://echarts.baidu.com/. Accessed 27 Feb 2018
Yin, F.J., Xiao, W.D., Ge, B., et al.: Incremental algorithm for clustering texts in internet-oriented topic detection. Appl. Res. Comput. 28(1), 54–57 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, M., Cheng, J., Guo, L., Li, P., Zhang, X., Wang, H. (2018). TwiTracker: Detecting and Extracting Events from Twitter for Entity Tracking. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-04503-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04502-9
Online ISBN: 978-3-030-04503-6
eBook Packages: Computer ScienceComputer Science (R0)