Advertisement

TwiTracker: Detecting and Extracting Events from Twitter for Entity Tracking

  • Meng XuEmail author
  • Jiajun Cheng
  • Lixiang Guo
  • Pei Li
  • Xin Zhang
  • Hui Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)

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.

Keywords

Twitter TwiTracker Event extraction Event fusion 

References

  1. 1.
    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 2012Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    González-Bailón, S., Borge-Holthoefer, J., Rivero, A., Moreno, Y.: The dynamics of protest recruitment through an online network. Sci. Rep. 1, 197 (2011)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Echarts Homepage. http://echarts.baidu.com/. Accessed 27 Feb 2018
  9. 9.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Meng Xu
    • 1
    Email author
  • Jiajun Cheng
    • 1
  • Lixiang Guo
    • 1
  • Pei Li
    • 1
  • Xin Zhang
    • 1
  • Hui Wang
    • 1
  1. 1.Science and Technology on Information Systems Engineering Laboratory, College of Systems EngineeringNational University of Defense TechnologyChangshaPeople’s Republic of China

Personalised recommendations