Understanding the predictive power of social media

Please check this box if you do not wish your email address to be published Acknowledgments: The authors would like to thank the anonymous reviewers for their valuable comments that have enabled the improvement of manuscript's quality. The authors would also like to acknowledge that the Before that, he served as a Researcher Grade D at the research center CERTH/ITI and at research center NCSR " Demokritos ". He was also founder and manager of the eGovernment Unit at Archetypon SA, an international IT company. He holds a Diploma in Electrical Engineering from the National Technical University of Athens, Greece, and an MSc and PhD from Brunel University, UK. During the past years he has initiated and managed several research projects (e.g. Automation. He has about 200 research publications in the areas of software modeling and development for the domains of eGovernment, eBusiness, eLearning, eManufacturing etc. Structured Abstract: Purpose The purpose of this article 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. Design/methodology/approach The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analyzed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyze this literature, hence gaining new insights into the field. Findings 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. Value 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. Purpose: The purpose of this article 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. Design/methodology/approach: The scientific literature was systematically reviewed …