Mining Brand Perceptions from Twitter Social Networks

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towards monitoring such perceptions (e.g., surveys) are costly and time consuming, and their results may quickly become outdated.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 .

A multidisciplinary perspective of big data in management research

“…In addition, it is understudied in prior studies that what roles the firms play in the digital marketing campaign and their engagement in the online communication activities is an interesting area to be explored in future. Andrews et al(2015); Fong et al(2015); Ghose and Han(2014); Grewal et al(2016); Li and Du(2012); Luo et al(2014); Shankar et al(2016) Digital and social media advertising Ghose and Todri-Adamopoulos(2016); Gopinath et al(2013); Iyer and Katona(2015); Järvinen and Karjaluoto(2015); Kumar et al(2017); ; Liu and Mattila(2017); Malthouse et al(2013); Mariani et al(2016); Trusov et al(2016) Brand analysis Camiciottoli et al(2014); Culotta and Cutler(2016); Godey et al(2016); Gretry et al(2017); Hamilton et al(2016); Költringer and Dickinger(2015); Moro et al(2016); Nam and Kannan(2014); Nguyen et al(2015); Pournarakis et al(2017); Schweidel and Moe(2014); Tirunillai and Tellis(2014); Electronic word-of-mouth Balaji et al(2016); Cantallops and Salvi(2014); Cascio et al(2015); Chen and Xie (2005); Eisingerich et al(2015); Hennig-Thurau et al(2015); Kim et al(2015); King et al(2014); ; Lee and Song(2010); Mayzlin (2006); …”

Editorial—Marketing Science and Big Data

“…For example, one paper in this special issue provides a fully-automated method to monitor brand-related messages on Twitter (Culotta and Cutler 2016). Another processes video data to make recommendations for new garment purchases (Lu et al 2016).…”

New vistas for marketing strategy: digital, data-rich, and developing market (D3) environments

“…For example, traditional methods of monitoring and validating perceptions using surveys become untenable when we gather millions of observations about a firm on social media. Culotta and Cutler (2016) develop a fully automated method for inferring attribute-specific brand perception ratings on Twitter (Social Perception Score). Ringel and Skiera (2016) develop a new method to feature-reduce and comprehensively visualize hundreds of competitive market structures in the LED TV market.…”