Exploring Web Search Behavior Patterns to Personalize the Search Results

2011 ◽  
Author(s):  
Walisa Romsaiyud ◽  
Wichian Premchaiswadi
Keyword(s):  
Web Search ◽  
Search Behavior ◽  
Behavior Patterns ◽  
Search Results

What is popular on Wikipedia and why?

First Monday ◽  
2007 ◽  
Author(s):  
Anselm Spoerri
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Search Behavior ◽  
Search Queries ◽  
Search Results ◽  
The Web

This paper analyzes which pages and topics are the most popular on Wikipedia and why. For the period of September 2006 to January 2007, the 100 most visited Wikipedia pages in a month are identified and categorized in terms of the major topics of interest. The observed topics are compared with search behavior on the Web. Search queries, which are identical to the titles of the most popular Wikipedia pages, are submitted to major search engines and the positions of popular Wikipedia pages in the top 10 search results are determined. The presented data helps to explain how search engines, and Google in particular, fuel the growth and shape what is popular on Wikipedia.

The Query is Just the Beginning

2012 ◽  
pp. 95-113
Author(s):  
Veronica Maidel ◽  
Dmitry Epstein
Keyword(s):  
Decision Making ◽  
Young Adults ◽  
Web Search ◽  
Search Behavior ◽  
Contextual Knowledge ◽  
Online Activity ◽  
Incremental Search ◽  
Search Results ◽  
Decision Making Processes ◽  
User Centric

Web search has become an integral part of everyday online activity. Existing research on search behavior offers an extensive and detailed account of what searchers do when they encounter the search results pages. Yet, there is limited inquiry into what drives the particular search decisions that are being made and what contextual factors drive this behavior. This study provides a user-centric inquiry focused on in-depth detailed investigation of search-related decision-making processes. It builds on data collected through analysis of structured observations of young adults performing searches on their personal laptops. It focuses explicitly on the decisions the users make after completing a query and facing a list of search results. The study reveals a pattern of sophisticated use of a variety of explicit cues, tacit and contextual knowledge, as well as employment of an incremental search strategy.

scholarly journals Characterizing the Influence of Confirmation Bias on Web Search Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Masaki Suzuki ◽  
Yusuke Yamamoto
Keyword(s):  
Health Literacy ◽  
Web Search ◽  
User Study ◽  
Confirmation Bias ◽  
Search Behavior ◽  
Prior Beliefs ◽  
Search Results ◽  
Search Tasks ◽  
Significant Difference ◽  
The Relationship

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.

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.

scholarly journals Analyzing Web Search Behavior for Software Engineering Tasks

2020 ◽  
Author(s):  
Nikitha Rao ◽  
Chetan Bansal ◽  
Thomas Zimmermann ◽  
Ahmed Hassan Awadallah ◽  
Nachiappan Nagappan
Keyword(s):  
Software Engineering ◽  
Web Search ◽  
Search Behavior
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