Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage |
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| Institution: | 1. MIT Center for Collective Intelligence, 245 First Street, 02142, Cambridge, MA, USA;2. University of Perugia, Department of Engineering, Via G. Duranti 93, 06125, Perugia, Italy;3. Galaxyadvisors AG, Laurenzenvorstadt 69, CH-5000 Aarau, Switzerland;4. Department of Economics and Management, Free University of Bozen-Bolzano, Universitätsplatz 1 - Piazza Università 1, 39100, Bozen-Bolzano, Italy;1. MIT Center for Collective Intelligence, 245 First Street, 02142, Cambridge, MA, USA;2. University of Perugia, Department of Engineering, Via G. Duranti 93, 06125, Perugia, Italy;3. Galaxyadvisors AG, Laurenzenvorstadt 69, CH-5000 Aarau, Switzerland;4. Department of Economics and Management, Free University of Bozen-Bolzano, Universitätsplatz 1 - Piazza Università 1, 39100, Bozen-Bolzano, Italy;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Room 1103, Bldg 24, ANU College of Business and Economics, Copland Building 24, The Australian National University, ACT, 2601, Australia;2. Dean, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-051, United Arab Emirates;3. Chair of Marketing and Entrepreneurship, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-049, United Arab Emirates;1. IS&A Area, Indian Institute of Management Tiruchirappalli, India;2. CEO & Commissioner, e-Governance, Government of Tamil Nadu, India;1. Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), University of Oklahoma, National Weather Center, 120 David L. Boren Blvd., Suite 2100, Norman, Oklahoma 73072, USA;2. Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, 47907, USA;1. School of Management, Fudan University, Shanghai, 200433, China;2. School of Economics and Management, Tsinghua University, Beijing, 100084, China |
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| Abstract: | 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. |
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| Keywords: | Virtual tribes Marketing Twitter Text mining Social network analysis |
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