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Twitter as a predictive system: A systematic literature review

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  • Cano-Marin, Enrique
  • Mora-Cantallops, Marçal
  • Sánchez-Alonso, Salvador

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.

Suggested Citation

  • Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sánchez-Alonso, Salvador, 2023. "Twitter as a predictive system: A systematic literature review," Journal of Business Research, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:jbrese:v:157:y:2023:i:c:s0148296322010268
    DOI: 10.1016/j.jbusres.2022.113561
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    References listed on IDEAS

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    1. Makaya, Christian & Blanco, Cristina & Barrédy, Céline, 2023. "Towards an ecological approach for interaction management in entrepreneurship courses," Journal of Business Research, Elsevier, vol. 160(C).

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