DOI: 10.1080/1369118X.2020.1776367
Terbit pada 24 Juni 2020 Pada Information, Communication & Society

Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries

S. Geiss Birgit Stark M. Steiner + 1 penulis

Abstrak

ABSTRACT Search engines are important political news sources and should thus provide users with diverse political information – an important precondition of a well-informed citizenry. The search engines’ algorithmic content selection strongly influences the diversity of the content received by the users – particularly since most users highly trust search engines and often click on only the first result. A widespread concern is that users are not informed diversely by search engines, but how far this concern applies has hardly been investigated. Our study is the first to investigate content diversity provided by five search engines on ten current political issues in Germany. The findings show that sometimes even the first result is highly diverse, but in most cases, more results must be considered to be informed diversely. This unreliability presents a serious challenge when using search engines as political news sources. Our findings call for media policy measures, for example in terms of algorithmic transparency.

Artikel Ilmiah Terkait

The case for voter-centered audits of search engines during political elections

Eni Mustafaraj Emma Lurie Claire Devine

22 Januari 2020

Search engines, by ranking a few links ahead of million others based on opaque rules, open themselves up to criticism of bias. Previous research has focused on measuring political bias of search engine algorithms to detect possible search engine manipulation effects on voters or unbalanced ideological representation in search results. Insofar that these concerns are related to the principle of fairness, this notion of fairness can be seen as explicitly oriented toward election candidates or political processes and only implicitly oriented toward the public at large. Thus, we ask the following research question: how should an auditing framework that is explicitly centered on the principle of ensuring and maximizing fairness for the public (i.e., voters) operate? To answer this question, we qualitatively explore four datasets about elections and politics in the United States: 1) a survey of eligible U.S. voters about their information needs ahead of the 2018 U.S. elections, 2) a dataset of biased political phrases used in a large-scale Google audit ahead of the 2018 U.S. election, 3) Google's "related searches" phrases for two groups of political candidates in the 2018 U.S. election (one group is composed entirely of women), and 4) autocomplete suggestions and result pages for a set of searches on the day of a statewide election in the U.S. state of Virginia in 2019. We find that voters have much broader information needs than the search engine audit literature has accounted for in the past, and that relying on political science theories of voter modeling provides a good starting point for informing the design of voter-centered audits.

The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

M. Makhortykh R. Ulloa Aleksandra Urman

3 Mei 2021

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,” “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.

More diverse, more politically varied: How social media, search engines and aggregators shape news repertoires in the United Kingdom

Antonis Kalogeropoulos R. Nielsen R. Fletcher

7 Juli 2021

There is still much to learn about how the rise of new, ‘distributed’, forms of news access through search engines, social media and aggregators are shaping people’s news use. We analyse passive web tracking data from the United Kingdom to make a comparison between direct access (primarily determined by self-selection) and distributed access (determined by a combination of self-selection and algorithmic selection). We find that (1) people who use search engines, social media and aggregators for news have more diverse news repertoires. However, (2) social media, search engine and aggregator news use is also associated with repertoires where more partisan outlets feature more prominently. The findings add to the growing evidence challenging the existence of filter bubbles, and highlight alternative ways of characterizing people’s online news use.

A Comparison of Source Distribution and Result Overlap in Web Search Engines

Sebastian Sünkler Helena Häußler D. Lewandowski + 1 lainnya

15 Juli 2022

When it comes to search engines, users generally prefer Google. Our study aims to find the differences between the results found in Google compared to other search engines. We compared the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US. Google displays more unique domains in the top results than its competitors. Wikipedia and news websites are the most popular sources overall. With some top sources dominating search results, the distribution of domains is also consistent across all search engines. The overlap between Google and Bing is always under 32%, while Metager has a higher overlap with Bing than DuckDuckGo, going up to 78%. This study shows that the use of another search engine, especially in addition to Google, provides a wider variety in sources and might lead the user to find new perspectives.

How search engines disseminate information about COVID-19 and why they should do better

Aleksandra Urman R. Ulloa M. Makhortykh

11 Mei 2020

Access to accurate and up-to-date information is essential for individual and collective decision making, especially at times of emergency. On February 26, 2020, two weeks before the World Health Organization (WHO) officially declared the COVID-19’s emergency a “pandemic,” we systematically collected and analyzed search results for the term “coronavirus” in three languages from six search engines. We found that different search engines prioritize specific categories of information sources, such as government-related websites or alternative media. We also observed that source ranking within the same search engine is subjected to randomization, which can result in unequal access to information among users.

Daftar Referensi

0 referensi

Tidak ada referensi ditemukan.

Artikel yang Mensitasi

0 sitasi

Tidak ada artikel yang mensitasi.