Creating personas from large amounts of online data is useful but difficult with manual methods.
To address this difficulty, we present Automatic Persona Generation (APG), which is an implementation of a methodology for quantitatively generating data-driven personas from online social media data.
APG is functional, and it is deployed with several organizations in multiple industry verticals.
APG employs a scalable web front-end user interface and robust back-end database framework processing tens of millions of user interactions with tens of thousands of online digital products across multiple online platforms, including Facebook, Google Analytics, and YouTube.
APG identifies audience segments that are both distinct and impactful for an organization to create persona profiles. APG enhances numerical social media data with relevant human attributes, such as names, photos, topics, etc. Here, we discuss the architecture development and central system features.
Overall, APG can benefit organizations in distributing content via online platforms or with online content that relates to commercial products. APG is unique in its algorithmic approach to processing social media data for customer insights. APG can be found online at https://persona.qcri.org.
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Jung, S.G., Salminen, J., and Jansen, B. J. (2020) Giving Faces to Data: Creating Data-Driven Personas from Personified Big Data. ACM Conference on Intelligent User Interfaces (IUI2020)(Demo Paper), Cagliari, Italy. 17-20 March, 132-133
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