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Advisor(s)
Abstract(s)
In this study, we focused on analyzing customer-generated data on
Facebook to explore how textual content on a social web can provide valuable
information for decision support. To accomplish this goal, we used several
techniques that included social network analysis (SNA), natural language
processing (NLP), data mining (DM), and machine learning (ML), integrating them
with artificial intelligence approaches. Our analysis aimed to harness the
information generated during the Volkswagen pollutant emissions situation in a case
study that was conducted using the textual content from 10,642 posts, that
represented the interactions of 25,877 users over a span of twenty-two weeks. The
results demonstrated that monitoring online social networks (OSNs) can
significantly enhance decision-making processes and might help to mitigate
potential damages to brands/businesses. By leveraging the proposed methodological
approach, a set of orientations for decision-making was extracted, providing
valuable guidance for brand management and reputation protection. Overall, this
study highlights the importance of analyzing textual content on OSNs and
leveraging advanced computational techniques to improve decision support.
Description
Keywords
Social network analysis Natural language processing Data mining Decision support Machine learning Artificial intelligence
Citation
Publisher
IOS Press