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Event

Data Science Forum | Prof. Dr. Cornelius Puschmann

Organizer: Data Science Center
Location: Zoom
Start Time: 22. April 2021, 12:00
End Time: 22. April 2021, 13:00

About the Data Science Forum
Das Data Science Forum des Data Science Centers (DSC) bietet Wissenschaftler*innen aller Disziplinen die Gelegenheit, ihre Forschung, Interessen und Herausforderungen im Bereich Data Science vor einem interdisziplinären Publikum zu präsentieren und zu diskutieren. Dabei können alle Themen rund um Data Science angesprochen werden, von generellen Aspekten im Umgang mit Big Data über die Methoden- und Technologieentwicklung bis hin zur Anwendung von z.B. künstlicher Intelligenz in verschiedenen Forschungsfeldern oder der Untersuchung rechtlicher, ethischer und sozialer Faktoren.

Zoom-Link zum Event
Kalendereintrag

About the Talk
More information coming soon!

About the Speaker
Cornelius Puschmann is Professor of Communication and Media Studies with a focus on Digital Communication at ZeMKI, Centre for Media, Communication and Information Sciences. In 2012, Cornelius was awarded a four-year personal grant from the Deutsche Forschungsgemeinschaft (DFG) for the project “Networking, visibility, information: a study of digital genres of scholarly communication and the motives of their users” at the Berlin School of Library and Information Science (BSLIS). From 2015 to 2016 he also served as visiting professor of digital communication at Zeppelin University in Friedrichshafen. From March to October 2016 served as a project leader at the Alexander von Humboldt Institute for Internet and Society (HIIG) in Berlin as part of the project “Networks of Outrage”, funded by the VolkswagenStiftung under its data journalism funding scheme. From 2016 to 2019 he was a senior researcher and coordinator of the postdoc research group Algorithmed Public Spheres (APS) at the Leibniz Institute for Media Research.

Cornelius Puschmann studies the use of digital media (search engines, social media) through a combination of social science methods and approaches from “computational social science”, i.e. text mining, sentiment analysis and network analysis.