{"id":9024,"date":"2022-09-26T17:05:53","date_gmt":"2022-09-26T15:05:53","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/lecture-series-science-of-science-in-the-spotlight-computational-social-science-bias-detection-and-theory-validation\/"},"modified":"2022-09-26T17:05:53","modified_gmt":"2022-09-26T15:05:53","slug":"lecture-series-science-of-science-in-the-spotlight-computational-social-science-bias-detection-and-theory-validation","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/lecture-series-science-of-science-in-the-spotlight-computational-social-science-bias-detection-and-theory-validation\/","title":{"rendered":"Lecture Series: Science Of Science in the Spotlight Computational Social Science: bias detection and theory validation"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>Using computational methods to study social structure and behavior at scale requires researchers to make a plethora of decisions, including how to sample and preprocess data, implement algorithms, and validate results. Jana Diesner presents findings and lessons learned from her group\u2019s work on assessing the impact of some of these choices, especially related to data provenance and selecting variables and metrics, on understanding social systems and validating social science theories in contemporary settings.<\/p><p>Julia Diesner highlights sources of biases and strategies for mitigating biased insights. Bringing this work into application contexts, she discusses how we leveraged computational <\/p><p>social science approaches to study the impact of information, science, and funding on society, and highlight some of our research in crisis informatics.<\/p><p>Info &#038; contact: <a href=\"mailto:jennifer.dusdal@uni.lu\" target=\"_self\" title=\"\" rel=\"noopener\">jennifer.dusdal@uni.lu<\/a><\/p><p>Registration: <a href=\"mailto:scisci@uni.lu\" target=\"_self\" title=\"\" rel=\"noopener\">scisci@uni.lu<\/a><\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>Using computational methods to study social structure and behavior at scale requires researchers to make a plethora of decisions, including how to sample and preprocess data, implement algorithms, and validate results. Jana Diesner presents findings and lessons learned from her group\u2019s work on assessing the impact of some of these choices, especially related to data provenance and selecting variables and metrics, on understanding social systems and validating social science theories in contemporary settings.Julia Diesner highlights sources of biases and strategies for mitigating biased insights. Bringing this work into application contexts, she discusses how we leveraged computational social science approaches to study the impact of information, science, and funding on society, and highlight some of our research in crisis informatics.<\/p>\n","protected":false},"author":0,"featured_media":9025,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2022-10-22 15:30:00","event_end_date":"2022-10-22 17:00:00","event_speaker_name":"Jana Diesner, Ass. Professor of Information Sciences, University of Illinois Urbana-Champaign School of Information Sciences, USA","event_speaker_link":"","event_is_online":false,"event_location":"Campus Belval, Maison des Sciences Humaines, Black Box\r\nRemote Webex Session  Meeting number: 2731 366 0273 \/ Password: SciSci","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[147,226],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lecture Series: Science Of Science in the Spotlight Computational Social Science: bias detection and theory validation - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"Using computational methods to study social structure and behavior at scale requires researchers to make a plethora of decisions, including how to sample and preprocess data, implement algorithms, and validate results. 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