The Language of Brands in Social Media

Abstract

This article highlights how social media data and language analysis can help managers understand brand positioning and brand competitive spaces to enable them to make various strategic and tactical decisions about brands. The authors use the output of topic models at the brand level to evaluate similarities between brands and to identify potential cobrand partners. In addition to using average topic probabilities to assess brands’ relationships to each other, they incorporate a differential language analysis framework, which implements scientific inference with multi-test-corrected hypothesis testing, to evaluate positive and negative topic correlates of brand names. The authors highlight the various applications of these approaches in decision making for brand management, including the assessment of brand positioning and future cobranding partnerships, design of marketing communication, identification of new product introductions, and identification of potential negative brand associations that can pose a threat to a brand's image. Moreover, they introduce a new metric, "temporal topic variability," that can serve as an early warning of future changes in consumer preference. The authors evaluate social media analytic contributions against offline survey data. They demonstrate their approach with a sample of 193 brands, representing a broad set of categories, and discuss its implications.

Source: Vanitha Swaminathan, Andrew Schwartz, Rowan Menezes, and Shawndra Hill, https://journals.sagepub.com/doi/10.1177/10949968221088275?icid=int.sj-abstract.citing-articles.3
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