BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/Los_Angeles X-WR-TIMEZONE:America/Los_Angeles BEGIN:VEVENT UID:48@escience.washington.edu DTSTART;TZID=America/Los_Angeles:20231010T163000 DTEND;TZID=America/Los_Angeles:20231010T172000 DTSTAMP:20231006T174228Z URL:https://escience.washington.edu/events/uwdss-teblunthuis/ SUMMARY:UW Data Science Seminar: Nathan TeBlunthuis DESCRIPTION:\n\nPlease join us for a UW Data Science Seminar on Tuesday\, October 10th from 4:30 to 5:20 p.m. PST. The seminar will feature Nathan T eBlunthuis\, a Postdoctoral Research Fellow at the Information School at t he University of Michigan. \n\n\nThis event will take place in the Physic s/Astronomy Auditorium 102 (PAA A102) on the University of Washington camp us.\n\n \;\n"Misclassification in Automated Content Analysis Causes Bi as in Regression. Can We Fix It? Yes We Can!"\nAbstract: Automated classif iers (ACs)\, often built via supervised machine learning (SML)\, can categ orize large\, statistically powerful samples of data ranging from text to images and video. They have become widely popular measurement devices in c omputational social science and related fields. Despite this popularity\, even highly accurate classifiers make errors that cause misclassification bias and misleading results when input to downstream statistical analyses —unless such analyses account for these errors. As we show in a systemat ic literature review of SML applications\,  scholars largely ignore miscl assification bias.\n\nIn principle\, existing statistical methods can use "gold standard" validation data\, such as that created by human annotators \, to correct misclassification bias. We introduce and test such methods\, including a new method we design and implement in the R package "misclass ification models"\, via Monte Carlo simulations designed to reveal each me thod's limitations\, which we also release. Based on our results\, we reco mmend our new error correction method as it is versatile and efficient. In sum\, automated classifiers\, even those below common accuracy standards or those making systematic misclassifications\, can be useful for measurem ent with careful study design and appropriate error correction methods.\n\ nBio: Dr. Nathan TeBlunthuis is a Postdoctoral Research Fellow in the Info rmation School at the University of Michigan. He was previously at the Dep artment of Communication Studies at Northwestern University and completed his PhD in Communication at the University of Washington. He is computatio nal social scientist who studies how collective action is organized in pro jects like Wikipedia\, online communities like Reddit\, and social movemen ts. An important part of his work is to improve the measurement of meanin gful communication behaviors from unstructured data such as text and multi media.\nThe UW Data Science Seminar is an annual lecture series at the U niversity of Washington that hosts scholars working across applied areas o f data science\, such as the sciences\, engineering\, humanities and arts along with methodological areas in data science\, such as computer science \, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners\, governm ental agencies and industry.\nThe 2022-2023 seminars will be held in perso n\, and are free and open to the public. LOCATION:Physics/Astronomy Auditorium A102\, 3910 15th Ave NE\, Seattle\, W A\, United States X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=3910 15th Ave NE\, Seattle\ , WA\, United States;X-APPLE-RADIUS=100;X-TITLE=Physics/Astronomy Auditori um A102:geo:0,0 END:VEVENT BEGIN:VTIMEZONE TZID:America/Los_Angeles X-LIC-LOCATION:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR