Towards Automatic Persona Generation Using Social Media

Abstract

The use of personas is an interactive design technique with considerable potential for product and content development. However, personas have typically been viewed as fairly static. In this research, we implement an approach for creating personas in real time, based on automated analysis of actual social media data, integrating data from Facebook, Twitter, and YouTube channels for a large commercial organization. From Twitter, we gather user insights representing interests and viewpoints, leveraging approximately 195,000 follower profiles. From YouTube, we gather demographic data and topical interests, leveraging more than 188,000 subscriber profiles and millions of user interactions. From Facebook, we collect instances of hundreds of thousands of link sharing by more than 54,000 social media followers, specifically examining the domains these users share. We integrate the social media data from all three platforms in order to demonstrating that this data can be used to develop personas in real-time. The research results provide insights into competitive marketing, topical interests, and preferred system features for the users of the online news medium. Research implications are that personas can be generated in real-time, instead of being the result of a laborious, time-consuming development process.