Where Is the News Breaking? Towards a Location-Based Event Detection Framework for Journalists

  • Bahareh Rahmanzadeh Heravi
  • Donn Morrison
  • Prashant Khare
  • Stephane Marchand-Maillet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8326)


The rise of user-generated content (UCG) as a source of information in the journalistic lifecycle is driving the need for automated methods to detect, filter, contextualise and verify citizen reports of breaking news events. In this position paper we outline the technological challenges in incorporating UCG into news reporting and describe our proposed framework for exploiting UGC from social media for location-based event detection and filtering to reduce the workload of journalists covering breaking and ongoing news events. News organisations increasingly rely on manually curated UGC. Manual monitoring, filtering, verification and curation of UGC, however, is a time and effort consuming task, and our proposed framework takes a first step in addressing many of the issues surrounding these processes.


Event Detection Location extraction Citizen Journalism User Generated Content Social News Semantic News Social Web Semantic Web Linked Data Social Semantic Journalism 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM (1998)Google Scholar
  2. 2.
    Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th International Conference on World Wide Web, pp. 61–70. ACM (2010)Google Scholar
  3. 3.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: Real-world event identification on Twitter. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, ICWSM 2011 (2011)Google Scholar
  4. 4.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)Google Scholar
  5. 5.
    Chen, Y., Amiri, H., Li, Z., Chua, T.S.: Emerging topic detection for organizations from microblogs. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM (2013)Google Scholar
  6. 6.
    Diakopoulos, N., De Choudhury, M., Naaman, M.: Finding and assessing social media information sources in the context of journalism. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 2451–2460. ACM (2012)Google Scholar
  7. 7.
    DOMO, How Much Data is Created Every Minute? (retrieved October 16, 2013)
  8. 8.
    Elloumi, S., Jaoua, A., Ferjani, F., Semmar, N., Besançon, R., Jaam, J., Hammami, H.: General Learning Approach for Event Extraction: Case of Management Change event. Journal of Information Sciences (2012)Google Scholar
  9. 9.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370. Association for Computational Linguistics (2005)Google Scholar
  10. 10.
    Heravi, B.R., McGinnis, J.: A Framework for Social Semantic Journalism. In: First International IFIP Working Conference on Value-Driven Social & Semantic Collective Intelligence (VaSCo), at ACM Web Science 2013, Paris, France (May 2013)Google Scholar
  11. 11.
    Hromic, H., Karnstedt, M., Wang, M., Hogan, A., Belák, V., Hayes, C.: Event Planning in a Stream of Big Data. In: LWA Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML). Workshop at LWA: Lernen, Wissen, Adaption (2012)Google Scholar
  12. 12.
    Jurgens, D.: That’s What Friends are for: Inferring Location in Online Social Media Platforms Based on Social Relationships. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  13. 13.
    Kijak, E., Gravier, G., Gros, P., Oisel, L., Bimbot, F.: HMM based structuring of tennis videos using visual and audio cues. In: Proceedings of the 2003 International Conference on Multimedia and Expo, ICME 2003, vol. 3, p. III-309. IEEE (2003)Google Scholar
  14. 14.
    Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., Shook, E.: Mapping the global Twitter heartbeat: The geography of Twitter. First Monday 18(5) (2013) (n. pag. Web. August 9, 2013)Google Scholar
  15. 15.
    Macdonald, C., Ounis, I., Soboroff, I.: Overview of the TREC 2007 Blog Track. In: TREC, vol. 7, pp. 31–43 (2007)Google Scholar
  16. 16.
    Petrovic, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Proceedings of NAACL, vol. 10 (2010)Google Scholar
  17. 17.
    Qian, X., Liu, G., Wang, H., Li, Z., Wang, Z.: Soccer video event detection by fusing middle level visual semantics of an event clip. In: Qiu, G., Lam, K.M., Kiya, H., Xue, X.-Y., Kuo, C.-C.J., Lew, M.S. (eds.) PCM 2010, Part II. LNCS, vol. 6298, pp. 439–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Rahmanzadeh Heravi, B., Boran, M., Breslin, J.: Towards Social Semantic Journalism. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)Google Scholar
  19. 19.
    Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534. Association for Computational Linguistics (2011)Google Scholar
  20. 20.
    Ritter, A., Etzioni Mausam, O., Clark, S.: Open domain event extraction from Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 1104–1112. ACM, New York (2012), doi:10.1145/2339530.2339704CrossRefGoogle Scholar
  21. 21.
    Rosen, J.: Definition of Citizen Journalism (2008), (retrieved October 16, 2013)
  22. 22.
    Rui, Y., Gupta, A., Acero, A.: Automatically extracting highlights for TV baseball programs. In: Proceedings of the Eighth ACM International Conference on Multimedia, pp. 105–115. ACM (2000)Google Scholar
  23. 23.
    Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 723–732. ACM (2012)Google Scholar
  24. 24.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)Google Scholar
  25. 25.
    Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: ICWSM 2009, pp. 311–314 (2009)Google Scholar
  26. 26.
    Scellato, S., Noulas, A., Lambiotte, R., Mascolo, C.: Socio-Spatial Properties of Online Location-Based Social Networks. In: ICWSM, vol. 11, pp. 329–336 (2011)Google Scholar
  27. 27.
    Shirazi, A., Rohs, M., Schleicher, R., Kratz, S., Müller, A., Schmidt, A.: Real-time nonverbal opinion sharing through mobile phones during sports events. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 307–310. ACM (2011)Google Scholar
  28. 28.
    Sonderman, J.: One-third of adults under 30 get news on social networks now, (retrieved October 16, 2013)
  29. 29.
    Statisticbrain, Twitter Statistics,
  30. 30.
    Unankard, S., Li, X., Sharaf, M.A.: Location-Based Emerging Event Detection in Social Networks. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 280–291. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  31. 31.
    Van Rijsbergen, C.J.: Information Retrieval (1979) ISBN 0-408-70929-4Google Scholar
  32. 32.
    Weng, J., Lee, B.: Event detection in Twitter. In: Proc. of ICWSM 2011, pp. 401–408 (2011)Google Scholar
  33. 33.
    Weng, J., Lee, B.-S.: Event Detection in Twitter. In: ICWSM (2011)Google Scholar
  34. 34.
    Xu, C., Zhang, Y.F., Zhu, G., Rui, Y., Lu, H., Huang, Q.: Using webcast text for semantic event detection in broadcast sports video. IEEE Transactions on Multimedia 10(7), 1342–1355 (2008)CrossRefGoogle Scholar
  35. 35.
    Xu, M., Maddage, N.C., Xu, C., Kankanhalli, M., Tian, Q.: Creating audio keywords for event detection in soccer video. In: Proceedings of the 2003 International Conference on Multimedia and Expo, ICME 2003, Vol. 2, pp. II-281. IEEE (2003)Google Scholar
  36. 36.
    YouTube statistics (2013), (retrieved October 16, 2013)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bahareh Rahmanzadeh Heravi
    • 1
  • Donn Morrison
    • 2
  • Prashant Khare
    • 1
  • Stephane Marchand-Maillet
    • 3
  1. 1.Digital Enterprise Research Institute (DERI)National University of IrelandGalwayIreland
  2. 2.Norwegian University of Science and TechnologyTrondheimNorway
  3. 3.University of GenevaSwitzerland

Personalised recommendations