Auto-tracking controversial topics in social-media-based customer dialog: A case study on Starbucks

Bei Yu, Yihan Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study proposed and validated a topic modeling-based approach for auto-tracking customer dialog on social media, using Starbucks as a case study because of its pioneering social media practice in service industry. A topic model was fit based on nearly 150,000 customer comments posted to Starbucks’ Facebook page in 2013. This model was able to identify not only business-related topics, such as customer responses to marketing campaigns, but also controversial topics regarding community involvement and corporate social responsibility, such as gay, gun, and government. Guided by this topic model, each topic’s evolving dynamics and patterns of user participation were further revealed, providing a bird’s-eye view of the topics and their evolution. The case study has demonstrated that the proposed approach can effectively track the main themes in the customer dialog on social media, zoom in on the controversial topics, measure their time spans, and locate the participants and the vocal activists. Such information would be valuable input for companies to design their intervention strategies and evaluate the outcomes in social media discussions.

Original languageEnglish (US)
Title of host publicationTransforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
PublisherSpringer Verlag
Pages87-96
Number of pages10
ISBN (Print)9783319781044
DOIs
StatePublished - Jan 1 2018
Event13th International Conference on Transforming Digital Worlds, iConference 2018 - Sheffield, United Kingdom
Duration: Mar 25 2018Mar 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Transforming Digital Worlds, iConference 2018
CountryUnited Kingdom
CitySheffield
Period3/25/183/28/18

Keywords

  • Customer dialog
  • Social media
  • Text mining
  • Topic modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yu, B., & Yu, Y. (2018). Auto-tracking controversial topics in social-media-based customer dialog: A case study on Starbucks. In Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings (pp. 87-96). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10766 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-78105-1_11