An Approach to Relevancy Detection: Contributions to the Automatic Detection of Relevance in Social Networks

  • Alvaro Figueira
  • Miguel Sandim
  • Paula Fortuna
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 444)


In this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.


Automatic Relevance detection Social networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Vieweg. Microblogged contributions to the emergency arena: Discovery, interpretation and implications. In Computer Supported Collaborative Work, February 2010Google Scholar
  2. 2.
    Marcelo Mendoza, Barbara Poblete, Carlos Castillo, Twitter under crisis: can we trust what we RT?, Proceedings of the First Workshop on Social Media Analytics, p.71-79, July 25-28, 2010, Washington D.C., District of Columbia [doi: 10.1145/1964858.1964869]
  3. 3.
    Akshay Java, Xiaodan Song, Tim Finin, Belle Tseng, Why we twitter: understanding microblogging usage and communities, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, p.56-65, August 12-12, 2007, San Jose, California [doi: 10.1145/1348549.1348556]
  4. 4.
    Mor Naaman, Jeffrey Boase, Chih-Hui Lai, Is it really about me?: message content in social awareness streams, Proceedings of the 2010 ACM conference on Computer supported cooperative work, February 06-10, 2010, Savannah, Georgia, USA [doi: 10.1145/1718918.1718953]
  5. 5.
  6. 6.
    Haewoon Kwak, Changhyun Lee, Hosung Park, Sue Moon, What is Twitter, a social network or a news media?, Proceedings of the 19th international conference on World wide web, April 26-30, 2010, Raleigh, North Carolina, USA [doi: 10.1145/1772690.1772751]
  7. 7.
    Vasileios Lampos, Tijl De Bie, Nello Cristianini, Flu detector: tracking epidemics on twitter, Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, September 20-24, 2010, Barcelona, SpainGoogle Scholar
  8. 8.
    Jagan Sankaranarayanan, Hanan Samet, Benjamin E. Teitler, Michael D. Lieberman, Jon Sperling, TwitterStand: news in tweets, Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 04-06, 2009, Seattle, Washington [doi: 10.1145/1653771.1653781]
  9. 9.
    Takeshi Sakaki, Makoto Okazaki, Yutaka Matsuo, Earthquake shakes Twitter users: real-time event detection by social sensors, Proceedings of the 19th international conference on World wide web, April 26-30, 2010, Raleigh, North Carolina, USA [doi: 10.1145/1772690.1772777]
  10. 10.
    Ana-Maria Popescu, Marco Pennacchiotti, Detecting controversial events from twitter, Proceedings of the 19th ACM international conference on Information and knowledge management, October 26-30, 2010, Toronto, ON, Canada [doi: 10.1145/1871437.1871751]
  11. 11.
    Michael Mathioudakis, Nick Koudas, TwitterMonitor: trend detection over the twitter stream, Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, June 06-10, 2010, Indianapolis, Indiana, USA [doi: 10.1145/1807167.1807306]
  12. 12.
    Bertrand De Longueville, Robin S. Smith, Gianluca Luraschi, “OMG, from here, I can see the flames!”: a use case of mining location based social networks to acquire spatio-temporal data on forest fires, Proceedings of the 2009 International Workshop on Location Based Social Networks, November 03-03, 2009, Seattle, Washington [doi: 10.1145/1629890.1629907]
  13. 13.
    A. J. Flanagin and M. J. Metzger. Perceptions of internet information credibility. Journalism and Mass Communication Quarterly, 77(3):515–540, 2000.Google Scholar
  14. 14.
    A. J. Flanagin and M. J. Metzger. The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media Society, 9(2):319–342, April 2007.Google Scholar
  15. 15.
    C. L. Armstrong and M. J. Mcadams. Blogs of information: How gender cues and individual motivations influence perceptions of credibility. Journal of Computer-Mediated Communication, 14(3):435–456, 2009.Google Scholar
  16. 16.
    F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting Spammers on Twitter. In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS), July 2010Google Scholar
  17. 17.
    J. Ratkiewicz, M. Conover, M. Meiss, B. Gonçalves, S. Patil, A. Flammini, and F. Menczer. Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams. arXiv, Nov 2010Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CRACS/INESC TEC and University of PortoPortoPortugal

Personalised recommendations