Adam, Silke; Maier, Michaela; Makhortykh, Mykola; Urman, Aleksandra; Christner, Clara; de León, Ernesto; Gil Lopez, Teresa (29 July 2021). Pushing research on user-centric information exposure forward: bringing tracking, survey and automated text classification together (Unpublished). In: IC2S2. Zurich. 27.07.-31.07.2021.
Full text not available from this repository.The abundance of choices in today’s information environment challenges traditional survey research for measuring exposure. In this presentation we will put forward suggestions on how we can improve our understanding of individuals’ media diets, i.e. the channels and contents chosen, in a digital world. Based on the limitations of existing user-centric tracking approaches, we introduce a new academic tracking solution. We then show how the combination of computational tracking and classical survey research, allows us not only to connect the offline and online realm, but also unravel the drivers and consequences of individuals’ information exposure. So far, however, this newly evolving strand of research is hampered by the fact that tracking data primarily capture the clicks and sources, without further looking at the content people are actually engaging with. We thus conclude this presentation by showing how automated text classification could enrich survey and tracking data and thereby lead to a paradigm shift in exposure research. Such a shift would allow us to better judge the degree to which societal phenomena like echo chambers or information avoidance truly exist.
Item Type: |
Conference or Workshop Item (Paper) |
|---|---|
Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB) |
UniBE Contributor: |
Adam, Silke, Makhortykh, Mykola, Urman, Aleksandra, de León Williams, Ernesto Emiliano |
Subjects: |
300 Social sciences, sociology & anthropology |
Language: |
English |
Submitter: |
Beatrice Romann |
Date Deposited: |
16 Nov 2021 10:02 |
Last Modified: |
05 Dec 2022 15:53 |
URI: |
https://boris.unibe.ch/id/eprint/160132 |
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