The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions.
The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field.
The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, analysis suggests that there is a clear need for more advanced sentiment analysis methods as well as methods for identifying search terms for collection and filtering of raw SM data.
The proposed framework enables researchers to classify and evaluate existing studies, to design scientifically rigorous new studies and to identify the field's weaknesses, hence proposing future research directions.
The authors would like to thank the anonymous reviewers for their valuable comments that have enabled the improvement of manuscript's quality. They would also like to acknowledge that the work presented in this paper has been partially funded by the European Union through the “Linked2Media – An Open Linked Data Platform for Semantically-Interconnecting Online, Social Media Leveraging Corporate Brand and Market Sector Reputation Analysis, FP7-SME-2011 No 286714” project.
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