How Big is the World you can Explore? A Study of Chinese College Students’ Search Behavior via Search Engines

Fu, Tao; Karan, Kavita

doi:10.1016/j.sbspro.2015.01.961

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…-Between 79.7% and 90.5% click advertisement rarely or none. The average BV difference between 20-24 & above-24 categories is less than 5.0% for questions [5][6][7][8][9][10] pairs have the same except question 6 and English CMPE & Turkish CMPE pair has the same except questions 6 and 8. The average BV difference between female & male categories is less than 5.0% for questions 6 and 12, between Turkish CMPE & SE categories it is the same for questions 9 and 12, and between English CMPE & SE categories it is the same only for question 12.…”

Section: Resultsmentioning

confidence: 99%

“…In the study of Fu and Karan [10], in order to investigate Chinese college students' search behavior on the search engines Baidu and Google, 257 Chinese college students studying in China and abroad filled out an online questionnaire at the beginning of 2014. The questionnaire was comprised of 27 questions and fill out process was completed approximately in two months.…”

Section: Introductionmentioning

confidence: 99%

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An analysis of user behaviors on the search engine results pages based on the demographic characteristics

2020

KSII TIIS

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The purpose of this survey-based study is to make an analysis of search engine users' behaviors on the Search Engine Results Pages (SERPs) based on the three demographic characteristics gender, age, and program studying. In this study, a questionnaire was designed with 12 closed-ended questions. Remaining questions other than the demographic characteristic related ones were about "tab", "advertisement", "spelling suggestion", "related query suggestion", "instant search suggestion", "video result", "image result", "pagination" and the amount of clicking results. The questionnaire was used and the data collected were analyzed with the descriptive statistics as well as the inferential statistics. 84.2% of the study population was reached. Some of the major results are as follows: Most of each demographic characteristic category (i.e. female, male, under-20, 20-24, above-24, English computer engineering, Turkish computer engineering, software engineering) have rarely or more click for tab, spelling suggestion, related query suggestion, instant search suggestion, video result, image result, and pagination. More than 50.0% of female category click advertisement rarely; however, for the others, 50.0% or more never click advertisement. For every demographic characteristic category, between 78.0% and 85.4% click 10 or fewer results. This study would be the first attempt with its complete content and design. Search engine providers and researchers would gain knowledge to user behaviors about the usage of the SERPs based on the demographic characteristics.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An analysis of user behaviors on the search engine results pages based on the demographic characteristics

2020

KSII TIIS

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“…Page rank bias has ethical implications, because search engine outcomes may impact people’s opinions, choices, and actions, and search engines may be intentionally used by stakeholders to influence society by attempts of censorship and search engine optimization ( Fu and Karan 2015 ; Baye et al, 2016 ). The influence of search engine should not be understated and thus identifying possible page rank biases is important from an ethical perspective ( Flaxman et al, 2016 ; Haim et al, 2018 ), and its page rank outcomes can nudge people toward certain opinions and decision ( Epstein and Robertson 2015 ) and reinforce possible stereotypical views on people and gender ( Kay et al, 2015 ; Otterbacher et al, 2017 ).…”

Section: Background Theory and Hypothesesmentioning

confidence: 99%

Search Engine Gender Bias

Wijnhoven

Haren

2021

Front. Big Data

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This article discusses possible search engine page rank biases as a consequence of search engine profile information. After describing search engine biases, their causes, and their ethical implications, we present data about the Google search engine (GSE) and DuckDuckGo (DDG) for which only the first uses profile data for the production of page ranks. We analyze 408 search engine screen prints of 102 volunteers (53 male and 49 female) on queries for job search and political participation. For job searches via GSE, we find a bias toward stereotypically “female” jobs for women but also for men, although the bias is significantly stronger for women. For political participation, the bias of GSE is toward more powerful positions. Contrary to our hypothesis, this bias is even stronger for women than for men. Our analysis of DDG does not give statistically significant page rank differences for male and female users. We, therefore, conclude that GSE’s personal profiling is not reinforcing a gender stereotype. Although no gender differences in page ranks was found for DDG, DDG usage in general gave a bias toward “male-dominant” vacancies for both men and women. We, therefore, believe that search engine page ranks are not biased by profile ranking algorithms, but that page rank biases may be caused by many other factors in the search engine’s value chain. We propose ten search engine bias factors with virtue ethical implications for further research.

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“…Search engines have been used to collect data 23,24 and reveal regional differences. 25,26 When searching through a search engine, many people stick to a single word or a sequential word group. Even though the searched information is the same, various searching queries may be created by people.…”

Section: Related Workmentioning

confidence: 99%

A framework for investigating search engines' stemming mechanisms: A case study on Bing

Şentürk

Gunduz

2021

Concurrency and Computation

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Big data attracts the attention of governments and a lot of companies today. The developments in technology and the Internet make it one of the important sources of big data. It is easy to get lost in the enormous amount of information contained on the Internet if there were no search engines. Knowing how the search engines work will be helpful to access the desired information. This work aims to be a guide for accessing the right information and also to help to understand search engine stemming and indexing algorithm for interested parties. In this article, we have developed a framework that could be used to investigate the stemming mechanisms of search engines. Our framework also uses Word2vec to analyze semantic relations. We have used our framework to investigate the stemming algorithm of the search engine Bing for English language.In order to achieve that we have used this framework to select words, create queries, send them to Bing, and finally analyze the millions of returned results. We have discussed the results in the context of our article. The results indicate that our framework is useful for analyzing the stemming mechanisms of search engines.

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How Big is the World you can Explore? A Study of Chinese College Students’ Search Behavior via Search Engines

Cited by 6 publications

References 24 publications

An analysis of user behaviors on the search engine results pages based on the demographic characteristics

An analysis of user behaviors on the search engine results pages based on the demographic characteristics

Search Engine Gender Bias

A framework for investigating search engines' stemming mechanisms: A case study on Bing

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