Mining Brand Perceptions from Twitter Social Networks

Aron Culotta () and Jennifer Cutler ()
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Aron Culotta: Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois 60616
Jennifer Cutler: Kellogg School of Management, Northwestern University, Evanston, Illinois 60208

Marketing Science, 2016, vol. 35, issue 3, 343-362

Abstract: Consumer perceptions are important components of brand equity and therefore marketing strategy. Segmenting these perceptions into attributes such as eco-friendliness, nutrition, and luxury enable a fine-grained understanding of the brand’s strengths and weaknesses. Traditional approaches towards monitoring such perceptions (e.g., surveys) are costly and time consuming, and their results may quickly become outdated. Extant data mining methods are unsuitable for this goal, and generally require extensive hand-annotated data or context customization, which leads to many of the same limitations as direct elicitation. Here, we investigate a novel, general, and fully automated method for inferring attribute-specific brand perception ratings by mining the brand’s social connections on Twitter. Using a set of over 200 brands and three perceptual attributes, we compare the method’s automatic ratings estimates with directly-elicited survey data, finding a consistently strong correlation. The approach provides a reliable, flexible, and scalable method for monitoring brand perceptions, and offers a foundation for future advances in understanding brand-consumer social media relationships.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0968 .

Keywords: social media; brand image; market structure; Twitter; attribute ratings; perceptual maps; big data; data mining; social networks (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (48)

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