Makhortykh, Mykola; Urman, Aleksandra; Ulloa, Roberto (26 March 2022). Memory, counter-memory, and denialism: How search algorithms select information about the Holodomor (Unpublished). In: 3rd Annual Taras Shevchenko Conference. Indiana University (Bloomington). 25.03.-27.03.2022.
Full text not available from this repository.Web search engines, such as Google or Yandex play a key role in today's media environment. These algorithmic systems shape social reality by informing their users about current and historical phenomena. However, there are many questions concerning the quality of information provided by the search engines, in particular when dealing with complex issues which are often prone to(geopolitical) contestation.
In our paper, we examine how search engine algorithms select and prioritize information about the Holodomor. Besides being one of the most tragic episodes in Ukraine's history, this instance of man-made famine is a subject of mnemonic conflicts. Despite being recognized as an act of genocide in Ukraine and other countries, official memory politics in Russia tends to downplay the importance of the Holodomor or deny its genocidal nature. This mnemonic contestation is complicated by the parallels drawn between the Holodomor and the Holocaust within Ukrainian memory politics, which are often criticized as a form of appropriation of Holocaust memory.
Against the backdrop of these mnemonic complexities, search engines serve as memory mediators which shape how the Holodomor is remembered in Ukraine and worldwide. Thus, we aim to understand how search algorithms select information about the Holodomor and whether such selection can be subjected to bias (e.g., in the case of Yandex which is sometimes assumed to promote pro-Kremlin narratives). To do so, we conduct an automated agent-based audit of the six search engines (Google, Bing, Yahoo, Baidu, Yandex, and DuckDuckGo). We examine the top 20 text search results for 160 agents collected using the query “Holodomor” in English and Russian in February 2020 and March 2021. Then, we use a combination of content analysis and historical analysis to identify the source type for each result as well as the quality of information provided by it.
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 300 Social sciences, sociology & anthropology 900 History |
Language: |
English |
Submitter: |
Mykola Makhortykh |
Date Deposited: |
21 Jun 2022 10:44 |
Last Modified: |
05 Dec 2022 16:20 |
Uncontrolled Keywords: |
Ukraine, memory, Holodomor, algorithms, algorithm audit, search engine, history, bias, propaganda |
URI: |
https://boris.unibe.ch/id/eprint/170685 |
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