Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda

As the quantity of social and online analytics data has drastically increased, a wide variety of methods are deployed to make sense of this data, typically via computational and algorithmic approaches. However, in many cases, these approaches trade one form of complexity for another by ignoring the principles of human cognitive processing. In this perspective manuscript, we propose an approach of employing Personas as an alternative form of making large volumes of online user analytics information useful to end users of the user and customer analytics, with results applicable in software development, business sectors, communication industry, and other domains where understanding online user behavior is deemed important. Toward this end, we have developed a system that automatically generates data-driven Personas from social media and online analytics data, capable of handling hundreds of millions of user interactions from tens of thousands of pieces of content on YouTube, Facebook and Google Analytics, while retaining the privacy of individual users of those channels. Our approach (1) identifies and prioritizes user segments by their online behavior, (2) associates the segments with demographic data, and (3) creates rich Persona profiles by dynamically adding characteristics, such as names, photos, and descriptive quotes. This chapter characterizes the currently open research problems in automatic Persona generation, such as de-aggregation of data, cross-platform data mapping, filtering of toxic comments, and choosing the right information content according to end-user needs. Addressing these problems requires the use of state-of-the-art techniques of computer and information science within one system and benefits greatly from inter-disciplinary collaboration. Overall, the research agenda set in this work aims at achieving the vision for automatic user profiling using diverse online and social media platforms and advanced data processing methods for the end goal of making complex analytics data more useful for human decision makers, especially those working with online content.