I See a Car Crash: Real-Time Detection of Small Scale Incidents in Microblogs

  • Axel Schulz
  • Petar Ristoski
  • Heiko Paulheim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)


Microblogs are increasingly gaining attention as an important information source in emergency management. Nevertheless, it is still difficult to reuse this information source during emergency situations, because of the sheer amount of unstructured data. Especially for detecting small scale events like car crashes, there are only small bits of information, thus complicating the detection of relevant information.

We present a solution for a real-time identification of small scale incidents using microblogs, thereby allowing to increase the situational awareness by harvesting additional information about incidents. Our approach is a machine learning algorithm combining text classification and semantic enrichment of microblogs. An evaluation based shows that our solution enables the identification of small scale incidents with an accuracy of 89% as well as the detection of all incidents published in real-time Linked Open Government Data.


Training Dataset Situational Awareness Incident Type Link Open Data Semantic Enrichment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abel, F., Hauff, C., Houben, G.J., Stronkman, R., Tao, K.: Twitcident: Fighting Fire with Information from Social Web Stream. In: Proceedings of International Conference on Hypertext and Social Media (2012)Google Scholar
  2. 2.
    Agarwal, P., Vaithiyanathan, R., Sharma, S., Shroff, G.: Catching the long-tail: Extracting local news events from twitter. In: Proceedings of the Sixth International Conference on Weblogs and Social Media, ICWSM 2012, Dublin, Ireland (2012)Google Scholar
  3. 3.
    Goolsby, R.: Lifting Elephants: Twitter and Blogging in Global Perspective. In: Social Computing and Behavioral Modeling, pp. 1–6. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Heim, P., Thom, D.: SemSor: Combining Social and Semantic Web to Support the Analysis of Emergency Situations. In: Proceedings of the 2nd Workshop on Semantic Models for Adaptive Interactive Systems SEMAIS. Springer, Heidelberg (2011)Google Scholar
  5. 5.
    Hienert, D., Wegener, D., Paulheim, H.: Automatic classification and relationship extraction for multi-lingual and multi-granular events from wikipedia. In: Detection, Representation, and Exploitation of Events in the Semantic Web. CEUR-WS, vol. 902, pp. 1–10 (2012)Google Scholar
  6. 6.
    Jadhav, A., Wang, W., Mutharaju, R., Anantharam, P.: Twitris: Socially Influenced Browsing. In: Demo at 8th International Semantic Web Conference on Semantic Web Challenge 2009, Washington, DC, USA (2009)Google Scholar
  7. 7.
    Krstajic, M., Rohrdantz, C., Hund, M., Weiler, A.: Getting There First: Real-Time Detection of Real-World Incidents on Twitter. In: Proceedings of 2nd IEEE Workshop on Interactive Visual Text Analytics (2012)Google Scholar
  8. 8.
    Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.C.: Tedas: A twitter-based event detection and analysis system. In: 2011 11th International Conference on ITS Telecommunications (ITST), pp. 1273–1276. IEEE Computer Society (2012)Google Scholar
  9. 9.
    Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval, pp. 117–120. Cambridge University Press (2009)Google Scholar
  10. 10.
    Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, pp. 227–236. ACM, New York (2011)CrossRefGoogle Scholar
  11. 11.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia Spotlight: Shedding Light on the Web of Documents. In: Proceedings of the 7th International Conference on Semantic Systems (I-Semantics), Graz, Austria. ACM (2011)Google Scholar
  12. 12.
    Okolloh, O.: Ushahidi, or ’testimony’: Web 2.0 tools for crowdsourcing crisis information. Participatory Learning and Action 59, 65–70 (2008)Google Scholar
  13. 13.
    Paulheim, H., Frnkranz, J.: Unsupervised Feature Generation from Linked Open Data. In: International Conference on Web Intelligence, Mining, and Semantics, WIMS 2012 (2012)Google Scholar
  14. 14.
    Sakaki, T., Okazaki, M.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860 (2010)Google Scholar
  15. 15.
    Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J., Mühlhäuser, M.: A multi-indicator approach for geolocalization of tweets. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM (2013)Google Scholar
  16. 16.
    Schulz, A., Ortmann, J., Probst, F.: Getting user-generated content structured: Overcoming information overload in emergency management. In: Proceedings of 2012 IEEE Global Humanitarian Technology Conference (GHTC 2012), pp. 1–10 (2012)Google Scholar
  17. 17.
    Schulz, A., Paulheim, H., Probst, F.: Crisis Information Management in the Web 3.0 Age. In: Proceedings of the Information Systems for Crisis Response and Management Conference (ISCRAM 2012), pp. 1–6 (2012)Google Scholar
  18. 18.
    Strötgen, J., Gertz, M.: Multilingual and cross-domain temporal tagging. Language Resources and Evaluation (2012)Google Scholar
  19. 19.
    Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 1079–1088. ACM, New York (2010)Google Scholar
  20. 20.
    Wanichayapong, N., Pruthipunyaskul, W., Pattara-Atikom, W., Chaovalit, P.: Social-based traffic information extraction and classification. In: 11th International Conference on ITS Telecommunications (ITST), pp. 107–112 (2011)Google Scholar
  21. 21.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques. Elsevier, Morgan Kaufman, Amsterdam, Netherlands (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Axel Schulz
    • 1
    • 2
  • Petar Ristoski
    • 1
  • Heiko Paulheim
    • 3
  1. 1.SAP ResearchGermany
  2. 2.Telecooperation Lab.Technische Universität DarmstadtGermany
  3. 3.Data and Web Science GroupUniversity of MannheimGermany

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