Intentionally Biasing User Representation?: Investigating the Pros and Cons of Removing Toxic Quotes from Social Media Personas

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Algorithmically generated personas can help organizations understand their social media audiences. However, when using algorithms to create personas from social media user data, the resulting personas may contain toxic quotes that negatively affect content creators’ perceptions of the personas. To address this issue, we have implemented toxicity detection in an algorithmic persona generation system capable […]

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Persona Preparedness: A Survey Instrument for Measuring the Organizational Readiness for Deploying Personas

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Organizational user-centric design is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations

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How does varying the number of personas affect user perceptions and behavior? Challenging the ‘small personas’ hypothesis!

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Studies in human-computer interaction recommend creating fewer than ten personas based on stakeholders’ limitations to cognitively process and use personas. However, no existing studies offer empirical support for having fewer rather than more personas. Investigating this matter, thirty-seven participants interacted with five and fifteen personas using an interactive persona system, choosing one persona to design

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Big Data, Small Personas: How Algorithms Shape the Demographic Representation of Data-Driven User Segments

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Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from

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Persona Analytics: a pathway to persona science

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Slidedeck on Persona Analytics: pathway to persona science from QUANT UX Conference 2022 https://drive.google.com/file/d/1euyQPqUdVd0B8r249SApNOyN6hzESf2n/view Salminen, J., Jung, S.G., and Jansen, B. J. (2022) Persona Analytics: a pathway to persona science. In CN Chapman, KZ Xu, M Callegaro, F Gao, and M Cipollone, eds. (2022). Proceedings of the 2022 Quantitative User Experience Conference (Quant UX Con).

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Personas as a Exploratory Data Analysis Technique

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Exploratory data analysis (EDA) explores and analyzes a data set using descriptive statistics and visualizations to learn about the dataset. The main motivations for EDA are: detecting data errors testing assumptions about the data selecting appropriate models determining relationships among the explanatory variables accessing the direction and magnitude relationships between explanatory and outcome variables. However,

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