Kuznetsova, Elizaveta; Makhortykh, Mykola; Urman, Aleksandra; Ulloa, Roberto (28 May 2022). Media Representations and Bias in Search Engines: Framing of the Russian COVID-19 Vaccine (Unpublished). In: 72nd Annual ICA Conference - "One world, one network?!". Paris, France. 26.05.-30.05.2022.
Full text not available from this repository. (Request a copy)Web search engines have become trusted sources of information and news which shape representation of social reality worldwide. However issues are represented differently across various engines, potentially leading to information inequalities and biases. In our paper, we use a mixed-method approach to investigate how search engines select news about the Russian COVID-19 and whether such selection is subjected to biases. Using a combination of framing and agent-based algorithmic auditing, we investigate media representations of the vaccine prioritized by five search engines: Google, Bing, Yahoo!, Duck-Duck-Go, and Baidu. Our preliminary findings based on the analysis of all unique stories (N=71) retrieved by search engines highlight inequalities in frame distribution, with Google and Yahoo promoting ‘disinformation’ frame and Bing and DuckDuckGo favoring ‘neutral’ stories. We further discuss our observations in context of the implications for the future of the news ecosystem in the digital environment.
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: |
Makhortykh, Mykola, Urman, Aleksandra |
Subjects: |
000 Computer science, knowledge & systems 000 Computer science, knowledge & systems > 070 News media, journalism & publishing 300 Social sciences, sociology & anthropology |
Language: |
English |
Submitter: |
Mykola Makhortykh |
Date Deposited: |
23 Jun 2022 15:01 |
Last Modified: |
05 Dec 2022 16:20 |
Uncontrolled Keywords: |
COVID, Russia, vaccine, news, web search, algorithms, algorithm audit |
URI: |
https://boris.unibe.ch/id/eprint/170658 |
Actions (login required)
 |
Edit item |