The Matter of Chance: Auditing Web Search Results Related to the 2020
U.S. Presidential Primary Elections Across Six Search Engines
- URL: http://arxiv.org/abs/2105.00756v1
- Date: Mon, 3 May 2021 11:18:19 GMT
- Title: The Matter of Chance: Auditing Web Search Results Related to the 2020
U.S. Presidential Primary Elections Across Six Search Engines
- Authors: Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa
- Abstract summary: We look at the text search results for "us elections", "donald trump", "joe biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex.
Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We examine how six search engines filter and rank information in relation to
the queries on the U.S. 2020 presidential primary elections under the default -
that is nonpersonalized - conditions. For that, we utilize an algorithmic
auditing methodology that uses virtual agents to conduct large-scale analysis
of algorithmic information curation in a controlled environment. Specifically,
we look at the text search results for "us elections", "donald trump", "joe
biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo,
and Yandex, during the 2020 primaries. Our findings indicate substantial
differences in the search results between search engines and multiple
discrepancies within the results generated for different agents using the same
search engine. It highlights that whether users see certain information is
decided by chance due to the inherent randomization of search results. We also
find that some search engines prioritize different categories of information
sources with respect to specific candidates. These observations demonstrate
that algorithmic curation of political information can create information
inequalities between the search engine users even under nonpersonalized
conditions. Such inequalities are particularly troubling considering that
search results are highly trusted by the public and can shift the opinions of
undecided voters as demonstrated by previous research.
Related papers
- Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information Environment [0.0]
We study the extent to which government-maintained web domains are represented in the online electoral information environment.
We find that 71% of all occurrences of state, county, and local government sources were mistargeted.
We argue that ensuring the correct targeting of these sources to their respective constituents is a critical part of Google's role in facilitating access to authoritative and locally-relevant electoral information.
arXiv Detail & Related papers (2024-04-06T17:09:04Z) - User Attitudes to Content Moderation in Web Search [49.1574468325115]
We examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search.
We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results.
More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.
arXiv Detail & Related papers (2023-10-05T10:57:15Z) - Novelty in news search: a longitudinal study of the 2020 US elections [62.997667081978825]
We analyze novelty, a measurement of new items that emerge in the top news search results.
We find more new items emerging for election related queries compared to topical or stable queries.
We argue that such imbalances affect the visibility of political candidates in news searches during electoral periods.
arXiv Detail & Related papers (2022-11-09T08:42:37Z) - Personalization of Web Search During the 2020 US Elections [0.0]
We study the effect of user characteristics and behavior on search results in a politically relevant context.
We set up a population of 150 synthetic internet users who are randomly located across 25 US cities.
These users differ in their browsing preferences and political ideology, and they build up realistic browsing and search histories.
arXiv Detail & Related papers (2022-09-28T11:18:56Z) - Where the Earth is flat and 9/11 is an inside job: A comparative
algorithm audit of conspiratorial information in web search results [62.997667081978825]
We examine the distribution of conspiratorial information in search results across five search engines: Google, Bing, DuckDuckGo, Yahoo and Yandex.
We find that all search engines except Google consistently displayed conspiracy-promoting results and returned links to conspiracy-dedicated websites in their top results.
Most conspiracy-promoting results came from social media and conspiracy-dedicated websites while conspiracy-debunking information was shared by scientific websites and, to a lesser extent, legacy media.
arXiv Detail & Related papers (2021-12-02T14:29:21Z) - Exposing Query Identification for Search Transparency [69.06545074617685]
We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems.
We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
arXiv Detail & Related papers (2021-10-14T20:19:27Z) - Searching for Representation: A sociotechnical audit of googling for
members of U.S. Congress [2.4366811507669124]
10% of the top Google search results are likely to mislead California information seekers who use search to identify their congressional representatives.
70% of the misleading results appear in featured snippets above the organic search results.
factors identified include Google's heavy reliance on Wikipedia, the lack of authoritative, machine parsable, high accuracy data about the identity of elected officials based on geographic location, and the search engine's treatment of under-specified queries.
arXiv Detail & Related papers (2021-09-14T23:13:02Z) - Search Engine Similarity Analysis: A Combined Content and Rankings
Approach [6.69087470775851]
We present an analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo.
We developed a new similarity metric that leverages both the content and the ranking of search responses.
We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
arXiv Detail & Related papers (2020-11-01T23:57:24Z) - Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
We propose using the political diversity of a website's audience as a quality signal.
Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
arXiv Detail & Related papers (2020-07-16T02:13:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.