Who are your users?: comparing media professionals’ preconception of users to data-driven personas<\/a>. In Proceedings of the 29th Australian Conference on Computer-Human Interaction (OZCHI ’17), Brisbane, Australia, p. 602-606.\u00a028 Nov.-1 Dec.,<\/p>\n","protected":false},"excerpt":{"rendered":"One of the reasons for using personas is to align user understandings across project teams and sites. As part of a larger persona study, 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 … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-64","post","type-post","status-publish","format-standard","hentry","category-user-study"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":66,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/generating-cultural-personas-from-social-data-a-perspective-of-middle-eastern-users\/","url_meta":{"origin":64,"position":0},"title":"Generating Cultural Personas From Social Data: A Perspective of Middle Eastern Users","date":"July 28, 2022","format":false,"excerpt":"We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":60,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/findings-of-a-user-study-of-automatically-generated-personas\/","url_meta":{"origin":64,"position":1},"title":"Findings of a User Study of Automatically Generated Personas","date":"July 28, 2022","format":false,"excerpt":"We report findings and implications from a seminaturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organization\u2019s 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\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":36,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/comparing-persona-analytics-and-social-media-analytics-for-a-user-centric-task-using-eye-tracking-and-think-aloud\/","url_meta":{"origin":64,"position":2},"title":"Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud","date":"July 28, 2022","format":false,"excerpt":"We compare a data-driven persona system and an analytics system for efficiency and effectiveness for a user identification task. Findings from the 34-participant experiment show that the data-driven persona system affords faster task completion, is easier for users to engage with, and provides better user identification accuracy. Eye-tracking data indicates\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":62,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/fixation-and-confusion-investigating-eye-tracking-participants-exposure-to-information-in-personas\/","url_meta":{"origin":64,"position":3},"title":"Fixation and Confusion \u2013 Investigating Eye-tracking Participants\u2019 Exposure to Information in Personas","date":"July 28, 2022","format":false,"excerpt":"To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":24,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/persona-transparency-analyzing-the-impact-of-explanations-on-perceptions-of-data-driven-personas\/","url_meta":{"origin":64,"position":4},"title":"Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas","date":"July 28, 2022","format":false,"excerpt":"Computational techniques are becoming more common in persona development. However, users of personas may question the information in persona profiles because they are unsure of how it was created. This problem is especially vexing for data-driven personas because their creation is an opaque algorithmic process. In this research, we analyze\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":18,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/the-ability-of-personas-an-empirical-evaluation-of-altering-incorrect-preconceptions-about-users\/","url_meta":{"origin":64,"position":5},"title":"The Ability of Personas: An Empirical Evaluation of Altering Incorrect Preconceptions About Users","date":"July 28, 2022","format":false,"excerpt":"False preconceptions about users can result in poor design, product development, and marketing decisions, so rectifying these preconceptions is essential for organizations. This research quantitatively evaluates the ability of data-driven personas to alter decision makers\u2019 preconceptions about their online social media users. We conduct a within-participant experiment of 31 professionals\u2026","rel":"","context":"In "user study"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/64","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/comments?post=64"}],"version-history":[{"count":1,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/64\/revisions"}],"predecessor-version":[{"id":65,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/64\/revisions\/65"}],"wp:attachment":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/media?parent=64"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/categories?post=64"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/tags?post=64"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}