The power of prediction with social media

By Harald Schoen, Daniel Gayo-Avello, Panagiotis Takis Metaxas, Eni Mustafaraj, M. Strohmaier, Peter A. Gloor. 2013

227 citations recorded by the source corpus.

– Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. , – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided. , – A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data. , – This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. David García
  2. Frank Schweitzer
  3. M. Strohmaier
  4. Foster J. Provost
  5. Arnim Bleier
  6. Daizhuo Chen
  7. Rodney Graeme Duffett
  8. Nitesh V. Chawla

In Statistical and Nonlinear Physics

  1. David García
  2. Frank Schweitzer
  3. M. Strohmaier
  4. Foster J. Provost
  5. Rodney Graeme Duffett
  6. Nadia Burkart
  7. Marco F. Huber

Citations in the corpus