Leveraging Localized Social Media Insights for Industry Early Warning Systems

Juan Bernabé-Moreno, Álvaro Tejeda-Lorente, Carlos Porcel-Gallego and Enrique Herrera-Viedma
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Juan Bernabé-Moreno: Department of Computer Science and A.I., University of Granada Granada, Spain
Álvaro Tejeda-Lorente: Department of Computer Science and A.I., University of Granada Granada, Spain
Carlos Porcel-Gallego: #x2020;Department of Computer Science and A.I., University of Jaén, Jaén, Spain
Enrique Herrera-Viedma: Department of Computer Science and A.I., University of Granada Granada, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2018, vol. 17, issue 01, 357-385

Abstract: Social Media (SM) has become the easiest, cheapest and fastest channel for companies to identify the events that affect their customers. The geo-location capabilities of the SM interactions enable Early Warning Systems to alert not only when the quality of service decays, but also where and how many customers are impacted. In this paper we present a system and a set of supporting metrics that exploit the geo-localized SM stream, quantify the perceived impact of events, incidents, etc. on a particular area over time. Industrial service providers can add this perceptional perspective to their standard monitoring tools to enable a prompt and appropriate reaction, the decision-making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.

Keywords: Early-warning systems; localized social media; social media sensor; social media insights; polarity (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622017400016

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