apg research

Rethinking Personas for Fairness: Algorithmic Transparency and Accountability in Data-Driven Personas

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Algorithmic fairness criteria for machine learning models are gathering widespread research interest. They are also relevant in the context of data-driven personas that rely on online user data and opaque algorithmic processes.  Overall, while technology provides lucrative opportunities for the persona design practice, several ethical concerns need to be addressed to adhere to ethical standards […]

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Enriching Social Media Personas with Personality Traits: A Deep Learning Approach Using the Big Five Classes

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In research led by Jon Salminen, to predict the personality traits of data-driven personas, we apply an automatic persona generation methodology to generate 15 personas from the social media data of an online news organization. After generating the personas, we aggregate each personas’ YouTube comments and predict the “Big Five” personality traits of each persona from

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A Template for Data-Driven Personas: Analyzing 31 Quantitatively Oriented Persona Profiles

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Following the proliferation of personified big data and data science algorithms, data-driven user personas (DDPs) are becoming more common in persona design. However, the DDP templates are seemingly diverse and fragmented, prompting a need for a synthesis of the information included in these personas. In this research, led by Joni Salminen, analyzing 31 templates for

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Comparing and Contrasting 12 Persona Templates

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The following is a post from the APG Team’s summer 2020 intern, Jaad Mohammed. ——– Personas provide a vivid way for businesses to understand their customer segments, including customer needs. Personas afford this by representing information, such as demographics, interests and pain points etc., of the customer based on, hopefully, real market research data. Personas

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