The power of prediction with social media

Abstract

Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer and social scientists, economists, and statisticians – among others– many new opportunities for research exploration. Arguably, one of the most interesting lines of work is that of predicting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc. In fact, an average of one in seven research papers presented at the WWW, ICWSM and IEEE SocialCom Conferences between 2007 and 2012 contain the term “predict” in their title. This upward trend, starting from 0 in 2006 and reaching 18% in 2012, shows a significant interest of the research community in predicting with Social Media. But what can be successfully predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to be successful and avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of commonly accepted evaluation approaches. Yet, this research seems to have reached a sufficient level of interest and relevance to justify a dedicated section. This special section aims to shape a frame of important questions to be addressed in this field, and fill the gaps in current research with presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or current events based on user-generated content in social media.

DOI: 10.1108/IntR-06-2013-0115

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Cite this paper

@article{Schoen2013ThePO, title={The power of prediction with social media}, author={Harald Schoen and Daniel Gayo-Avello and Panagiotis Takis Metaxas and Eni Mustafaraj and Markus Strohmaier and Peter A. Gloor}, journal={Internet Research}, year={2013}, volume={23}, pages={528-543} }