User Generated Content: An Analysis of User Behavior by Mining Political Tweets

  • Rocío Abascal-Mena
  • Erick López-Ornelas
  • J. Sergio Zepeda-Hernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8029)


With the emergence of smarthphones and social networks, a very large proportion of communication takes place on short texts. This type of communication, often anonymous, has allowed a new public participation in political issues. In particular, electoral phenomena all over the world have been greatly influenced by these networks. In the recent elections in Mexico, Twitter became a virtual place to bring together scientists, artists, politicians, adults, youth and students trying to persuade people about the candidate: Andrés Manuel López Obrador (AMLO). Our research is based on the collection of all tweets sent before, during and after the presidential elections of July 1, 2012 in Mexico containing the hashtag #AMLO. The aim of this study is to analyze the behavior of users on three different times. We apply SentiWordNet 3.0 in order to know how user behavior changes depending of the political situation and whether this is reflected on the tweets.


user behavior sentiment analysis social web Twitter public participation web 2.0 user generated content 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proc. of 4th ICWSM, pp. 122–129. AAAI Press (2010)Google Scholar
  2. 2.
    Friedman, T.: Power to the (Blogging) People. New York Times (September 14, 2010),
  3. 3.
    Yusuf, H.: Old and New Media: Converging During the Pakistan Emergency (March 2007-February 2008). Massachusetts Institute of Technology, Center for Future Civic Media, Cambridge, Mass (2009),
  4. 4.
    Bollen, J., Pepe, A., Mao, H.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp. 450–453 (2011)Google Scholar
  5. 5.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185 (2010)Google Scholar
  6. 6.
    Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation, LREC 2006, Genova, Italy, pp. 417–422 (2006)Google Scholar
  7. 7.
    Fellbaum, C.: WordNet: An Electronical Lexical Database. The MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rocío Abascal-Mena
    • 1
  • Erick López-Ornelas
    • 1
  • J. Sergio Zepeda-Hernández
    • 1
  1. 1.Departamento de Tecnologías de la InformaciónUniversidad Autónoma Metropolitana – CuajimalpaMéxico D.F.México

Personalised recommendations