Bernard J. Jansen

Persona Generation from Aggregated Social Media Data

We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online user data for generation of persona descriptions. We present the overall methodological approach, data analysis process, and system development. Findings show this method can develop believable personas representing real groups of people using real-time online user data. Results have implications for those distributing products via online platforms.

Findings of a User Study of Automatically Generated Personas

We report findings and implications from a semi-naturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organizations social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the system in a use case scenario. We employed a variety of data collection methods, including mouse tracking and survey data, analyzing the data with a mixed method approach. Results show that having an interactive system may aid in keeping personas at the forefront while making customer-centric decisions and indicate that data-driven personas fulfill information needs of decision makers by mixing personas and numerical data. The findings have implications for the design of persona systems and the use of online analytics data to better understand users and customers.

Persona Perception Scale: Developing and Validating an Instrument for Human-Like Representations of Data

Personas are widely used in software development, system design, and HCI studies. Yet, their evaluation is difficult, and there are no recognized and validated measurement scales to date. To improve this condition, this research develops a persona perception scale based on reviewing relevant literature. We validate the scale through a pilot study with 19 participants, each evaluating three personas (57 evaluations in total). This is the first reported effort to systematically develop and validate an instrument for persona perception measurement. We find the constructs and items of the scale perform well, with factor loadings ranging between 0.60 and 0.95. Reliability, measured as Cronbachs Alpha, is also satisfactory, encouraging us to pursue the use of the scale with a larger sample in future work.

Automatic Persona Generation (APG): A Rationale and Demonstration

We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We present the overall methodological approach, architecture development, and main system features. APG has a potential value for organizations distributing content via online platforms and is unique in its approach to persona generation. APG can be found online at https://persona.qcri.org.

Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel

Social media channels with audiences in the millions are increasingly common. Efforts at segmenting audiences for populations of these sizes can result in hundreds of audience segments, as the compositions of the overall audiences tend to be complex. Although understanding audience segments is important for strategic planning, tactical decision making, and content creation, it is unrealistic for human decision makers to effectively utilize hundreds of audience segments in these tasks. In this research, we present efforts at simplifying the segmentation of audience populations to increase their practical utility. Using millions of interactions with hundreds of thousands of viewers with an organization’s online content collection, we first isolate the maximum number of audience segments, based on behavioral profiling, and then demonstrate a computational approach of using non-negative matrix factorization to reduce this number to 42 segments that are both impactful and representative segments of the overall population. Initial results are promising, and we present avenues for future research leveraging our approach.

Who are your users?: comparing media professionals' preconception of users to data-driven personas

1One of the reasons for using personas is to align user understandings across project teams and sites. As part of a larger persona study, at Al Jazeera English (AJE), we conducted 16 qualitative interviews with media producers, the end users of persona descriptions. We asked the participants about their understanding of a typical AJE media consumer, and the variety of answers shows that the understandings are not aligned and are built on a mix of own experiences, own self, assumptions, and data given by the company. The answers are sometimes aligned with the data-driven personas and sometimes not. The end users are divided in two groups: news producers who have little interest in having data-based insights of news consumers and producers for social media platforms who have more interest in this information.