• DocumentCode
    35592
  • Title

    Predicting Elections for Multiple Countries Using Twitter and Polls

  • Author

    Tsakalidis, Adam ; Papadopoulos, Symeon ; Cristea, Alexandra I. ; Kompatsiaris, Yiannis

  • Volume
    30
  • Issue
    2
  • fYear
    2015
  • fDate
    Mar.-Apr. 2015
  • Firstpage
    10
  • Lastpage
    17
  • Abstract
    The authors´ work focuses on predicting the 2014 European Union elections in three different countries using Twitter and polls. Past works in this domain relying strictly on Twitter data have been proven ineffective. Others, using polls as their ground truth, have raised questions regarding the contribution of Twitter data for this task. Here, the authors treat this task as a multivariate time-series forecast, extracting Twitter- and poll-based features and training different predictive algorithms. They´ve achieved better results than several past works and the commercial baseline.
  • Keywords
    government data processing; politics; social networking (online); European Union election prediction; Twitter-based feature extraction; ground truth value; multiple countries; multivariate time-series forecasting; poll-based feature extraction; Electronic voting; Feature extraction; Forecasting; Intelligent systems; Nominations and elections; Prediction algorithms; Predictive models; Twitter; Web mining; elections; intelligent systems; machine learning; time-series forecasting;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2015.17
  • Filename
    7021854