Abstract
Millions of people use Twitter daily, posting thousands of messages and interacting with their peers. This research aims to evaluate and classify the predictive potential of the Twitter social platform through the intelligent analysis of user-generated public big data analytics. A systematic literature review (SLR) covering Web of Science, IEEE, Scopus and other databases identified the gaps and opportunities for developing predictive applications of User-Generated Content (UGC) on Twitter since 2006. Our research is a practical contribution to the use of Twitter data as a predictive system. A wide variety of application domains, highlighting social network analysis and public health, have been identified by applying innovative techniques for conducting a massive SLR, leveraging machine learning and graph analysis. The results give rise to new research lines with implications for both scholars and business leaders.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
