Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2187980.2188267acmotherconferencesArticle/Chapter ViewAbstractPublication PageswebconfConference Proceedingsconference-collections
tutorial

"Making sense of it all": an attempt to aid journalists in analysing and filtering user generated content

Published: 16 April 2012 Publication History
  • Get Citation Alerts
  • Abstract

    This position paper explores how journalists can embrace new ways of content provision and authoring, by aggregating and analyzing content gathered from Social Media. Current challenges in the news media industry are reviewed and a new system for capturing emerging knowledge from Social Media is described. Novel features that assist professional journalists in processing sheer amounts of Social Media information are presented with a reference to the technical requirements of the system. First implementation steps are also discussed, particularly focusing in event detection and user influence identification.

    References

    [1]
    A. Stuart. "Citizen Journalism and the Rise of "Mass Self-Communication": Reporting the London Bombings". In Global Media Journal, Australian Edition, 1(1), 2007.
    [2]
    G. Brandstetter, and P. Hörschinger. "Journalism and Social Media". ikp PR and Lobbying GmbH, Vienna, 2010.
    [3]
    N. Newman. "The rise of social media and its impact on mainstream journalism: A study of how newspapers and broadcasters in the UK and US are responding to a wave of participatory social media, and a historic shift in control towards individual consumers", Reuters Institute for the Study of Journalism, University of Oxford, 2009.
    [4]
    S. Sizov. "GeoFolk: Latent Spatial Semantics in Web 2.0 Social Media". 3rd ACM Int'l Conf Web Search and Data Mining (WSDM), New York, USA, 2010.
    [5]
    A. Siebes, J. Vreeken, M. van Leeuwen. "Compression based frequent items set mining. Item sets that compress". In SDM 2006, pp. 393--404, 2006.
    [6]
    S. Papadopoulos, C. Zigkolis, Y. Kompatsiaris, A. Vakali. "Cluster-based Landmark and Event Detection on Tagged Photo Collections". IEEE Multimedia Magazine 18(1), pp. 52--63, 2011.
    [7]
    J. Yang, J. Leskovec. "Modeling Information Diffusion in Implicit Networks". IEEE Int'l Conf. On Data Mining, 2010.
    [8]
    D. Nadeau, S. Satoshi. "A survey of named entity recognition and classification". Linguisticae Investigationes 30(1), pp. 3--26, 2007.
    [9]
    V. Carchiolo, A. Longheu, M. Malgeri. "Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment". Inf. Sci. 180(10), pp. 1893--1907, 2010.
    [10]
    M. Brenner and E. Izquierdo." Mediaeval benchmark: Social event detection in collaborative photo collections". In Larson et al. Working Notes Proceedings of the MediaEval 2011 Workshop, Pisa, Italy, Sep 1--2, 2011, volume 807 of CEUR Workshop Proceedings. CEUR-WS.org, 2011.
    [11]
    S. Papadopoulos, C. Zigkolis, Y. Kompatsiaris, and A. Vakali. "Certh @ mediaeval 2011 social event detection task". In Larson et al. Working Notes Proceedings of the MediaEval 2011 Workshop, Pisa, Italy, Sep 1--2, 2011, volume 807 of CEUR Workshop Proceedings. CEUR-WS.org, 2011.
    [12]
    T. Sakaki, M. Okazaki, and Y. Matsuo. "Earthquake shakes twitter users: real-time event detection by social sensors". In Proceedings of the 19th international conference on World wide web, pages 851--860. ACM, 2010.
    [13]
    J. Weng and B.S. Lee. Event detection in twitter. 2011.
    [14]
    H. Sayyadi, M. Hurst, and A. Maykov. "Event detection and tracking in social streams". In E. Adar, M. Hurst, T. Finin, N. S. Glance, N. Nicolov, and B.L. Tseng, editors, ICWSM. The AAAI Press, 2009.
    [15]
    S. Wasserman, K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press. 1994.
    [16]
    K. Saito, M. Kimura, K. Ohara, and H. Motoda. Discovery of super-mediators of information diffusion in social networks. In Proceedings of the 13th international conference on Discovery science, DS'10, pages 144--158, Berlin, Heidelberg, 2010. Springer-Verlag
    [17]
    J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 807--816, New York, NY, USA, 2009. ACM.

    Cited By

    View all
    • (2022)A Human-Centered Design Approach to Creating Tools to Help Journalists Monitor Digital Political Ads: Insights and ChallengesDigital Journalism10.1080/21670811.2022.206432111:3(411-430)Online publication date: 29-Apr-2022
    • (2018)Who is Addressed in this Comment?Proceedings of the ACM on Human-Computer Interaction10.1145/32743362:CSCW(1-20)Online publication date: 1-Nov-2018
    • (2014)Social Multimedia Crawling for Mining and SearchComputer10.1109/MC.2014.13547:5(84-87)Online publication date: May-2014

    Index Terms

    1. "Making sense of it all": an attempt to aid journalists in analysing and filtering user generated content

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
      April 2012
      1250 pages
      ISBN:9781450312301
      DOI:10.1145/2187980
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • Univ. de Lyon: Universite de Lyon

      In-Cooperation

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 16 April 2012

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. journalism
      2. news
      3. sensor mining
      4. social indexing
      5. social media

      Qualifiers

      • Tutorial

      Conference

      WWW 2012
      Sponsor:
      • Univ. de Lyon
      WWW 2012: 21st World Wide Web Conference 2012
      April 16 - 20, 2012
      Lyon, France

      Acceptance Rates

      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)5
      • Downloads (Last 6 weeks)0

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)A Human-Centered Design Approach to Creating Tools to Help Journalists Monitor Digital Political Ads: Insights and ChallengesDigital Journalism10.1080/21670811.2022.206432111:3(411-430)Online publication date: 29-Apr-2022
      • (2018)Who is Addressed in this Comment?Proceedings of the ACM on Human-Computer Interaction10.1145/32743362:CSCW(1-20)Online publication date: 1-Nov-2018
      • (2014)Social Multimedia Crawling for Mining and SearchComputer10.1109/MC.2014.13547:5(84-87)Online publication date: May-2014

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media