Biased Representation of Politicians in Google and Wikipedia Search? The Joint Effect of Party Identity, Gender Identity and Elections

Pradel, Franziska

doi:10.1080/10584609.2020.1793846

Cited by 14 publications

(5 citation statements)

References 56 publications

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“…and Bing) restricted variation of sources across time, favoring those that were considered “reliable” to prevent the surfacing of “fake news.” Kawakami et al (2020) found that a year before the US elections 2020, the number of unique news in Google’s Top Stories differed for different candidates, and it was higher for Donald Trump, which was attributed to him being the incumbent president. Pradel (2021) found gender and party differences in the amount of personal information related to politicians that appear on the search suggestions before and after the elections. Closer to our work, Metaxa et al (2019) systematically analyzed daily search results, finding search outputs to be relatively stable, though some shifts suggested the existence of internal algorithmic factors, for example, monthly synchronization of Google servers.…”

Section: Search Engine Auditingmentioning

confidence: 94%

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

Ulloa

Makhortykh

Urman

et al. 2023

Social Science Computer Review

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The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.

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Section: Search Engine Auditingmentioning

confidence: 94%

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

Ulloa

Makhortykh

Urman

et al. 2023

Social Science Computer Review

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show abstract

“…Kalla and Aronow (2015) demonstrate that negative facts are more likely to be removed from such politicians' personal biographies compared to positive facts through experiments with humans. On the other hand, Pradel (2020) suggest that even political affiliation-revealed users place more emphasis on their self-identify as Wikipedia, the eco-system of the platforms mitigates the partisanship of the users. Umarova and Mustafaraj (2019), however, detected the polarization and the tension between users in Wikipedia political page editing.…”

Section: Related Workmentioning

confidence: 99%

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Matsui,

Miyazaki,

Murayama

2024

ICWSM

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In this era of digital politics, understanding the factors that influence the supply of political information is important. This study investigates the relationship between socio-economic status and the political information supplied on Wikipedia. To this end, it employs a dataset of politicians who ran for local elections in Japan over approximately 20 years and discovers that the creation and revisions of local politicians' pages are associated with socio-economic factors such as the employment ratio by industry and age distribution. We find that the majority of the suppliers of politicians' information are unregistered and primarily interested in politicians' pages compared to registered users. Additional analysis reveals that users who supply information about politicians before and after an election are more active on Wikipedia than the average user. The findings presented imply that the information supply on Wikipedia, which relies on voluntary contributions, may reflect regional socio-economic disparities.

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“…The possibility that outputs of web search engines can be systematically skewed towards certain gender and racial groups is increasingly recognized both by the broad public and information retrieval (IR) community [41]. Race and gender biases are found in different IR systems associated with search engines, including text search results [43,38] and search autocompletion [8,13]. However, image search is particularly relevant in this context, because of high interpretative and affective potential of visual information [12,34] that makes it a potent means of challenging, but also forming stereotypes.…”

Section: Related Work: Race and Gender Bias In Image Searchmentioning

confidence: 99%

Advances in Bias and Fairness in Information Retrieval

2021

Communications in Computer and Information Science

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Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.

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Biased Representation of Politicians in Google and Wikipedia Search? The Joint Effect of Party Identity, Gender Identity and Elections

Cited by 14 publications

References 56 publications

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Advances in Bias and Fairness in Information Retrieval

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