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Extracting brand information from social networks: Integrating image, text, and social tagging data

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  • Klostermann, Jan
  • Plumeyer, Anja
  • Böger, Daniel
  • Decker, Reinhold

Abstract

Images are an essential feature of many social networking services, such as Facebook, Instagram, and Twitter. Through brand-related images, consumers communicate about brands with each other and link the brand with rich contextual and consumption experiences. However, previous articles in marketing research have concentrated on deriving brand information from textual user-generated content and have largely not considered brand-related images. The analysis of brand-related images yields at least two challenges. First, the content displayed in images is heterogeneous, and second, images rarely show what users think and feel in or about the situations displayed. To meet these challenges, this article presents a two-step approach that involves collecting, labeling, clustering, aggregating, mapping, and analyzing brand-related user-generated content. The collected data are brand-related images, caption texts, and social tags posted on Instagram. Clustering images labeled via Google Cloud Vision API enabled to identify heterogeneous contents (e.g. products) and contexts (e.g. situations) that consumers create content about. Aggregating and mapping the textual information for the resulting image clusters in the form of associative networks empowers marketers to derive meaningful insights by inferring what consumers think and feel about their brand regarding different contents and contexts.

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  • Klostermann, Jan & Plumeyer, Anja & Böger, Daniel & Decker, Reinhold, 2018. "Extracting brand information from social networks: Integrating image, text, and social tagging data," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 538-556.
    • Handle: RePEc:eee:ijrema:v:35:y:2018:i:4:p:538-556
    DOI: 10.1016/j.ijresmar.2018.08.002
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    1. Moon, Sangkil & Kamakura, Wagner A., 2017. "A picture is worth a thousand words: Translating product reviews into a product positioning map," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 265-285.
    2. Aron Culotta & Jennifer Cutler, 2016. "Mining Brand Perceptions from Twitter Social Networks," Marketing Science, INFORMS, vol. 35(3), pages 343-362, May.
    3. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    4. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    5. Smith, Andrew N. & Fischer, Eileen & Yongjian, Chen, 2012. "How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 102-113.
    6. Wendy W. Moe & Oded Netzer & David A. Schweidel, 2017. "Social Media Analytics," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 483-504, Springer.
    7. Mafael, Alexander & Gottschalk, Sabrina A. & Kreis, Henning, 2016. "Examining Biased Assimilation of Brand-related Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 91-106.
    8. Böger, Daniel & Kottemann, Pascal & Meißner, Martin & Decker, Reinhold, 2017. "A mechanism for aggregating association network data: An application to brand concept maps," Journal of Business Research, Elsevier, vol. 79(C), pages 90-106.
    9. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    10. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    11. Petra Kralj Novak & Jasmina Smailović & Borut Sluban & Igor Mozetič, 2015. "Sentiment of Emojis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    Full references (including those not matched with items on IDEAS)

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