Validating social media data for automatic persona generation

Using personas during interactive design has considerable potential for product and content development. Unfortunately, personas have typically been a fairly static technique. In this research, we validate an approach for creating personas in real time, based on analysis of actual social media data in an effort to automate the generation of personas. We validate that social media data can be implemented as an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation that produces online digital content. Using the organization's YouTube channel, we collect demographic data, customer interactions, and topical interests, leveraging more than 188,000 subscriber profiles and more than 30 million user interactions. Then, we conduct statistical analysis on the social media data to determine whether the data could lead to the generation of valid personas based on statistically difference market segments. Findings show that customers can be segmented using product topics by gender and age based using social media data. However, our findings also show that the data is biased by the content created. The results offer insights into competitive marketing and product preferences for the consumers of the online digital content. Implications are that personas can be generated in real-time using social media data, instead of a time-consuming manual development process.