skip to main content
10.1145/2463728.2463762acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicegovConference Proceedingsconference-collections
research-article

Integrating argumentation technologies and context-based search for intelligent processing of citizens' opinion in social media

Published:22 October 2012Publication History

ABSTRACT

Nowadays, governments are adopting Web 2.0 technologies for interacting with citizens, empowering them to share their views, react to issues of their concern and form opinion. In particular, social media play an important role in this context, due to their widespread use. For governments, a major technical challenge is the lack of automated intelligent tools for processing citizens' opinion in government social media. At the same time, during the last decade, argumentation theory has consolidated itself in Artificial Intelligence as a new paradigm for modeling common sense reasoning, with application in several areas, such as legal reasoning, multiagent systems, and decision support systems, among others. This paper outlines an argument-based approach for overcoming such challenge, combined with context-based information retrieval. Our ultimate aim is to combine context-based search and argumentation in a collaborative framework for managing (retrieving and publishing) service- and policy-related information in government-use social media tools.

References

  1. O'Reilly, T. Government as a Platform. Innovations, vol 6, no.1, pp 13--40,2010.Google ScholarGoogle ScholarCross RefCross Ref
  2. DiMaio, A., Government 2.0: A Gartner Definition, 2009. http://blogs.gartner.eom/andrea_dimaio/2009/11/13/government-2-0-a-gartner-definition/, last retrieved 28 February 2012.Google ScholarGoogle Scholar
  3. Bonson, E., Torres, L., Royo, S., and Flores, F., Local e-Government 2.0: Social Media and Corporate Transparency in Municipalities, Government Information Quarterly, vol. 29, pp 123--132, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  4. Government of Singapore, Government Social Media Directory, available at: http://www.socialmedia.gov.sg/Web/Home/Default.aspx, last retrieved 15 April 2012.Google ScholarGoogle Scholar
  5. Bertot, J. C, Jaeger, P. T, and Hansen, D., The Impact of Polices on Government Social Media Usage: Issues, Challenges, and Recommendations, Government Information Quarterly, vol. 29, pp 30--40, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  6. Lorenzetti, C., Maguitman, A. A Semi-supervised Incremental Algorithm to Automatically Formulate Topical Queries. Information Science. Elsevier. 179 (12), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Simari, G., Rahwan, I. (eds), Argumentation in Artificial Intelligence, Springer Verlag, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Estévez, E., Chesñevar, C., Maguitman, A., Brena, R. DECIDE 2.0 -- A Framework for Intelligent Processing of Citizens' Opinion in Social Media. In Proc. dg.o 2012, Maryland, USA, pp.266--267, ACM Press, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Guo, L., Lease, M. Personalizing Local Search with Twitter. SIGIR 2011 Workshop on Enriching Information Retrieval (ENTR 2011). July 24--28, 2011, Beijing, China. Available at: http://select.cs.cmu.edu/meetings/enir2011/papers/guolease.pdf.Google ScholarGoogle Scholar
  10. Maguitman, A., Leake, D., Reichherzer, T., Suggesting novel but related topics: towards context-based support for knowledge model extension, Proc. of the 10th IUI Confi., San Diego, California, USA, January 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Twitter Sentiment Corpus, available at: http://www.sananalytics.com/lab/twitter-sentiment/, retrieved April 2012.Google ScholarGoogle Scholar
  12. Bing Liu. "Sentiment Analysis: A Multifaceted Problem." IEEE Intelligent Systems, 25(3), pp. 76--80, 2010.Google ScholarGoogle Scholar
  13. Maguitman, A., Leake, D., Reichherzer, T., Menczer, F. Dynamic Extraction of Topic Descriptors and Discriminators: Towards Automatic Context-Based Topic Search. Proceedings of the 13th CKM Conf.. ACM Press. Washington, DC, USA, November 2004 Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. García, A., Simari, G. Defeasible Logic Programming: An Argumentative Approach. Theory and Practice of Logic Programming 4(1-2): 95--138, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Chesñevar, C., Maguitman, A, Simari, G. Recommender Systems based on Argumentation, in "Emerging Artificial Intelligence Applications in Computer Engineering". Maglogiannis et al (eds). Frontiers in Artificial Intelligence and Applications, Vol. 160, pp. 53--70.. IOS Press, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Galitsky, B., McKenna, E. Sentiment Extraction from Consumer Reviews for Providing Product Recommendations. US Patent Application US 2009/0282019 A1Google ScholarGoogle Scholar

Index Terms

  1. Integrating argumentation technologies and context-based search for intelligent processing of citizens' opinion in social media

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            ICEGOV '12: Proceedings of the 6th International Conference on Theory and Practice of Electronic Governance
            October 2012
            547 pages
            ISBN:9781450312004
            DOI:10.1145/2463728

            Copyright © 2012 ACM

            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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 22 October 2012

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            ICEGOV '12 Paper Acceptance Rate23of98submissions,23%Overall Acceptance Rate350of865submissions,40%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader