Context Matcher: Improved Web Search Using Query Term Context in Source Document and in Search Results

2006 ◽  
pp. 486-497 ◽  
Author(s):  
Takahiro Kawashige ◽  
Satoshi Oyama ◽  
Hiroaki Ohshima ◽  
Katsumi Tanaka
Keyword(s):  
Web Search ◽  
Query Term ◽  
Search Results ◽  
Source Document

The Effects of Web Search Engine Query Ambiguity and Results Sorting Method on User Performance and Preference

2002 ◽  
Vol 46 (14) ◽  
pp. 1271-1275
Author(s):  
David N. Aurelio ◽  
Ronald R. Mourant
Keyword(s):  
Search Engine ◽  
Page Number ◽  
Web Search ◽  
User Performance ◽  
Query Term ◽  
Sorting Method ◽  
Search Results ◽  
Web Search Engine ◽  
Single Term ◽  
Insufficient Number

Users of Web search engines report two main problems: an insufficient number of relevant results and the mixing of relevant results with irrelevant results. Therefore, this research investigates the effects of query ambiguity and three forms of sorting search results on user performance and preference. Forty-eight Web search engine users evaluated three forms of sorting results. For each task, the query was a single term that had one, two, or three meanings. The results indicated that the preferred results sorting method was affected by the page number of the correct results. When the correct results were all located on the same page (i.e., the first page), the participants preferred the Ranking, Disambiguating, and Categories methods when the query term had one, two and three meanings, respectively. When the correct results were not on the first page, the test participants preferred the Categories sorting method for all number of query meanings.

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

2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Primary Elections ◽  
Virtual Agents ◽  
Search Results ◽  
Presidential Primary ◽  
Large Scale Analysis ◽  
Algorithmic Information

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.

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