Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/604
Title: Understanding the Predictive Power of Social Media
Authors: Kalampokis, Evangelos
Tambouris, Efthimios
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Social networks
data analysis
open data
world wide web
Issue Date: 2013
Source: Internet Research
Volume: 23
Issue: 5
First Page: 544
Last Page: 559
Abstract: Purpose 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. Design/methodology/approach 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. 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. Originality/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.
URI: https://doi.org/10.1108/IntR-06-2012-0114
https://ruomo.lib.uom.gr/handle/7000/604
ISSN: 1066-2243
Other Identifiers: 10.1108/IntR-06-2012-0114
Appears in Collections:Department of Applied Informatics

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