This paper present insights into whether it is possible to identify social media addicts by using big data analytical methods, and if such identification could create value for businesses and society.my. With these questions in mind, an extensive systematic review was composed in order to explore the knowledge basis, and find current knowledge gaps. Firstly, the current identification methods was explored and the takeaways from these discussed and taken into consideration as we started exploring big data possibilities. Our scope was changed to the identification of risk groups as research on the field, suggested that personal contact and hands-on work with a psychiatrist or other professionals is necessary for diagnosis. Pre-Scraped Twitter data was used as our data source in our work with big data. Different pre-processing methods was applied to arrive at insightful results that could be connected to existing identification methods through basic emotions. With these insights in mind, it was discussed how such identification could create value for society and businesses alike. The findings shows that it is possible to identify social media addicts with big data analytical methods, but that this would optimally involve direct approach with users in combination to this. Furthermore, both research and real world cases proves that there is value and competitive advantage to get from the use of big data.
|Educations||MSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis|
|Number of pages||133|