Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage
This addresses the lack of automated tools for firms to study consumer tribes for marketing strategy and for scholars to extend marketing research, though it appears incremental as it applies deep learning to a specific domain without claiming broad SOTA.
The paper tackles the problem of automatically identifying virtual tribes (E-tribes) from social media data by introducing Tribefinder, a system that analyzes Twitter users' tweets and language use to reveal tribal affiliations, with an example application across three macro-categories: alternative realities, lifestyle, and recreation.
Internet and social media offer firms novel ways of managing their marketing strategy and gain competitive advantage. The groups of users expressing themselves on the Internet about a particular topic, product, or brand are frequently called a virtual tribe or E-tribe. However, there are no automatic tools for identifying and studying the characteristics of these virtual tribes. Towards this aim, this paper presents Tribefinder, a system to reveal Twitter users' tribal affiliations, by analyzing their tweets and language use. To show the potential of this instrument, we provide an example considering three specific tribal macro-categories: alternative realities, lifestyle, and recreation. In addition, we discuss the different characteristics of each identified tribe, in terms of use of language and social interaction metrics. Tribefinder illustrates the importance of adopting a new lens for studying virtual tribes, which is crucial for firms to properly design their marketing strategy, and for scholars to extend prior marketing research.