BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20210503T144401Z DESCRIPTION:Inhalt \nPeoples’ activities and opinions recorded as digital traces online\, especially on social media and other web-based platforms\ , offer increasingly informative pictures of the public. They promise to a llow inferences about populations beyond the users of the platforms on whi ch the traces are recorded\, representing real potential for the Social Sc iences and a complement to survey-based research. But the use of digital t races brings its own complexities and new error sources to the research en terprise. Recently\, researchers have begun to discuss the errors that can occur when digital traces are used to learn about humans and social pheno mena.\nThis talk discusses various strategies for critical reflection on t he limitations\, implications\, and consequences of using digital traces f or measuring social constructs. Inspired by the Total Survey Error (TSE) F ramework developed for survey methodology\, we introduce a conceptual fram ework to diagnose\, understand\, and document errors that may occur in stu dies based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework\, the new “Total Error Fra mework for Digital Traces of Human Behavior on Online Platforms” (TED-On ) identifies several types of error that are specific to the use of digita l traces. By providing a standard vocabulary to describe these errors\, th e proposed framework and this talk advances communication and research con cerning the use of digital traces in scientific social research.\nVortrage nden\nDr. Fabian Flöck leads the Data Science team at the Computationa l Social Science Department at GESIS. He is interested in the validity and transparency of automated measurement in social science contexts\, but al so researches interactive data analysis services\, collaborative content c reation and digital communication processes. He studied communication scie nces and sociology\, and subsequently acquired a PhD in computer science.\ nIndira Sen is a doctoral candidate at GESIS\, working at the intersection of Computational Social Science and Natural Language Processing. She has a bachelor’s and master's degree in Computer Science\, and currently wor ks on measuring social constructs like political attitudes and hate speech from social media data and understanding the limitations inherent to this task.\n \n DTEND;TZID="W. Europe Standard Time":20210701T140000 DTSTAMP:20210413T081405Z DTSTART;TZID="W. Europe Standard Time":20210701T130000 LAST-MODIFIED:20210503T144401Z LOCATION:Zoom PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=de:Digital traces of Human Behaviour in Online Platforms - Research design and error sources (in English) TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000F03C44AB4D30D701000000000000000 0100000006348861AD66DDB4CAF29CBAC4A22CB60 X-ALT-DESC;FMTTYPE=text/html:

Inhalt

Peopl es’ activities and opinions recorded as digital traces online\, especial ly on social media and other web-based platforms\, offer increasingly info rmative pictures of the public. They promise to allow inferences about pop ulations beyond the users of the platforms on which the traces are recorde d\, representing real potential for the Social Sciences and a complement t o survey-based research. But the use of digital traces brings its own comp lexities and new error sources to the research enterprise. Recently\, rese archers have begun to discuss the errors that can occur when digital trace s are used to learn about humans and social phenomena.

This talk discusses various strategies for critical reflection on the limitations\, implicatio ns\, and consequences of using digital traces for measuring social constru cts. Inspired by the Total Survey Error (TSE) Framework developed for surv ey methodology\, we introduce a conceptual framework to diagnose\, underst and\, and document errors that may occur in studies based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework\, the new “Total Error Framework for Digital Traces o f Human Behavior on Online Platforms” (TED-On) identifies several types of error that are specific to the use of digital traces. By providing a st andard vocabulary to describe these errors\, the proposed framework and th is talk advances communication and research concerning the use of digital traces in scientific social research.

Vortragenden

Dr. Fabian Flöck leads the Data Science team at the Computational Social Science Department at GESIS. He is intere sted in the validity and transparency of automated measurement in social s cience contexts\, but also researches interactive data analysis services\, collaborative content creation and digital communication processes. He st udied communication sciences and sociology\, and subsequently acquired a P hD in computer science.

Indira Sen is a doctoral candidate at GESIS\, working at the intersection of Compu tational Social Science and Natural Language Processing. She has a bachelo r’s and master's degree in Computer Science\, and currently works on mea suring social constructs like political attitudes and hate speech from soc ial media data and understanding the limitations inherent to this task.

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