{"id":60,"date":"2022-07-28T09:27:54","date_gmt":"2022-07-28T09:27:54","guid":{"rendered":"https:\/\/metric.qcri.org\/blog\/?p=60"},"modified":"2022-07-28T09:27:55","modified_gmt":"2022-07-28T09:27:55","slug":"findings-of-a-user-study-of-automatically-generated-personas","status":"publish","type":"post","link":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/findings-of-a-user-study-of-automatically-generated-personas\/","title":{"rendered":"Findings of a User Study of Automatically Generated Personas"},"content":{"rendered":"\n<p>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.<\/p>\n\n\n\n<p>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.<\/p>\n\n\n\n<p>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.<\/p>\n\n\n\n<p>The findings have implications for the design of persona systems and the use of online analytics data to better understand users and customers.<\/p>\n\n\n\n<p>Salminen, J., Jung, S.G., An, J., Kwak, H., and\u00a0Jansen, B. J.\u00a0(2018)\u00a0<a rel=\"noreferrer noopener\" href=\"http:\/\/www.bernardjjansen.com\/uploads\/2\/4\/1\/8\/24188166\/lbw097.pdf\" target=\"_blank\">Findings of a User Study of Automatically Generated Personas<\/a>. ACM CHI Conference on Human Factors in Computing Systems (CHI2018) (Extended Abstract), Montr\u00e9al, Canada, 21-26 April, LBW097.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 system in a use case &#8230; <a title=\"Findings of a User Study of Automatically Generated Personas\" class=\"read-more\" href=\"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/findings-of-a-user-study-of-automatically-generated-personas\/\" aria-label=\"More on Findings of a User Study of Automatically Generated Personas\">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-60","post","type-post","status-publish","format-standard","hentry","category-user-study"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"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":60,"position":0},"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 &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":12,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/using-artificially-generated-pictures-in-customer-facing-systems-an-evaluation-study-with-data-driven-personas\/","url_meta":{"origin":60,"position":1},"title":"Using Artificially Generated Pictures in Customer-facing Systems: An Evaluation Study with Data-Driven Personas","date":"July 28, 2022","format":false,"excerpt":"We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"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":60,"position":2},"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 &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":231,"url":"https:\/\/metric.qcri.org\/blog\/2024\/06\/25\/benefits-of-metric-in-teaching-user-study-analytics\/","url_meta":{"origin":60,"position":3},"title":"Benefits of METRIC in Teaching User Study Analytics","date":"June 25, 2024","format":false,"excerpt":"Analytics Illustration [Source: ACUA] Various industries benefit from user analytic studies, both with and without knowledge. E-Commerce, logistics and transportation, hospitality, web development, real estate, healthcare, public services, and even education. Many industries need user feedback to improve their services, so implementing user analytic studies in academia benefits students. Here\u2026","rel":"","context":"In &quot;analytics&quot;","img":{"alt_text":"Benefits of METRIC in Teaching User Study Analytics","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2024\/06\/Screen-Shot-2024-06-13-at-5.59.38-PM.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":16,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/toxic-text-in-personas-an-experiment-on-user-perceptions\/","url_meta":{"origin":60,"position":4},"title":"Toxic Text in Personas: An Experiment on User Perceptions","date":"July 28, 2022","format":false,"excerpt":"When algorithms create personas from social media data, the personas can become noxious via automatically including toxic comments. To investigate how users perceive such personas, we conducted a 2 \u00d7 2 user experiment with 496 participants that showed participants toxic and non-toxic versions of data-driven personas. We found that participants\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":46,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/personas-and-analytics-a-comparative-user-study-of-efficiency-and-effectiveness-for-a-user-identification-task\/","url_meta":{"origin":60,"position":5},"title":"Personas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task","date":"July 28, 2022","format":false,"excerpt":"Personas are a well-known technique in human computer interaction. However, there is a lack of rigorous empirical research evaluating personas relative to other methods. In this 34-participant experiment, we compare a persona system and an analytics system, both using identical user data, for efficiency and effectiveness for a user identification\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/60","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=60"}],"version-history":[{"count":1,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/60\/revisions"}],"predecessor-version":[{"id":61,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/60\/revisions\/61"}],"wp:attachment":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/media?parent=60"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/categories?post=60"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/tags?post=60"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}