scholarly journals The impact of privacy indicators on search engine browsing patterns

2009 ◽  
Author(s):  
Janice Tsai ◽  
Serge Egelman ◽  
Lorrie Cranor ◽  
Alessandro Acquisti
Keyword(s):  
Search Engine ◽  
The Impact

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

The Impact of Search Engines on Virus Propagation

2021 ◽  
pp. 2150230
Author(s):  
Cai Fu ◽  
Zhaokang Ke ◽  
Yunhe Zhang ◽  
Xiwu Chen ◽  
Liqing Cao ◽  
...  
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Data Sets ◽  
Immune Mechanism ◽  
Virus Propagation ◽  
Immunization Strategy ◽  
Immune Mechanisms ◽  
Social Network Data ◽  
Information Engineering ◽  
The Impact

With the popularization of computers and the development of information engineering, the emergence of search engines makes it possible to get the information needed from big data quickly and efficiently. However, in recent years, a multiplicity of new viruses have been propagated by search engines. Many researchers choose to cut off the source of virus propagation, ignoring the virus immunization strategy based on the search engine. In this paper, we analyze the impact of search engines on virus propagation. First, considering the immune effect and cost, two kinds of immune mechanisms based on the search engine that have greater practicability are defined. Second, immune mechanisms based on the search engine are theoretically analyzed by the iteration method and the dynamic method. The results show that this immunization strategy can slow down or eliminate the propagation of a virus to a certain extent. Third, three real social network data sets are used to simulate and analyze the immune mechanism. We find that when the proportion of nodes being infected and the proportion of infected nodes being identified by the search engine satisfy a certain relationship, our immune mechanism can inhibit the spread of viruses, which confirms our theoretical analysis results.

scholarly journals Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

2019 ◽  
Vol 11 (9) ◽  
pp. 202 ◽  
Author(s):  
Rovira ◽  
Codina ◽  
Guerrero-Solé ◽  
Lopezosa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Of Science ◽  
Growth Potential ◽  
Google Scholar ◽  
Unexpected Finding ◽  
Ranking Algorithms ◽  
Microsoft Academic ◽  
Citation Counts ◽  
The Impact

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

scholarly journals A review of the impact of financing mechanisms on maternal health care in Australia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Haylee Fox ◽  
Stephanie M. Topp ◽  
Emily Callander ◽  
Daniel Lindsay
Keyword(s):  
Health Care ◽  
Maternal Health ◽  
Health System ◽  
Search Engine ◽  
Grey Literature ◽  
Maternal Health Care ◽  
World Health ◽  
Google Search ◽  
The Impact ◽  
Out Of Pocket Costs

Abstract Background The World Health Organization states there are three interrelated domains that are fundamental to achieving and maintaining universal access to care - raising sufficient funds for health care, reducing financial barriers to access by pooling funds in a way that prevents out-of-pocket costs, and allocating funds in a way that promotes quality, efficiency and equity. In Australia, a comprehensive account of the mechanisms for financing the health system have not been synthesised elsewhere. Therefore, to understand how the maternal health system is financed, this review aims to examine the mechanisms for funding, pooling and purchasing maternal health care and the influence these financing mechanisms have on the delivery of maternal health services in Australia. Methods We conducted a scoping review and interpretative synthesis of the financing mechanisms and their impact on Australia’s maternal health system. Due to the nature of the study question, the review had a major focus on grey literature. The search was undertaken in three stages including; searching (1) Google search engine (2) targeted websites and (3) academic databases. Executive summaries and table of contents were screened for grey literature documents and Titles and Abstracts were screened for journal articles. Screening of publications’ full-text followed. Data relating to either funding, pooling, or purchasing of maternal health care were extracted for synthesis. Results A total of 69 manuscripts were included in the synthesis, with 52 of those from the Google search engine and targeted website (grey literature) search. A total of 17 articles we included in the synthesis from the database search. Conclusion Our study provides a critical review of the mechanisms by which revenues are raised, funds are pooled and their impact on the way health care services are purchased for mothers and babies in Australia. Australia’s maternal health system is financed via both public and private sources, which consequentially creates a two-tiered system. Mothers who can afford private health insurance – typically wealthier, urban and non-First Nations women - therefore receive additional benefits of private care, which further exacerbates inequity between these groups of mothers and babies. The increasing out of pocket costs associated with obstetric care may create a financial burden for women to access necessary care or it may cause them to skip care altogether if the costs are too great.

scholarly journals The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2015 ◽  
Vol 112 (33) ◽  
pp. E4512-E4521 ◽  
Author(s):  
Robert Epstein ◽  
Ronald E. Robertson
Keyword(s):  
Search Engine ◽  
The United States ◽  
Demographic Characteristics ◽  
Attitudes And Beliefs ◽  
Controlled Experiments ◽  
Double Blind ◽  
Demographic Groups ◽  
Randomized Controlled ◽  
Democratic Elections ◽  
The Impact

Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India’s 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.

scholarly journals Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

2014 ◽  
Vol 60 (7) ◽  
pp. 1632-1654 ◽  
Author(s):  
Anindya Ghose ◽  
Panagiotis G. Ipeirotis ◽  
Beibei Li
Keyword(s):  
Consumer Behavior ◽  
Search Engine ◽  
The Impact

Search engine advertisements: The impact of advertising statements on click-through and conversion rates

2013 ◽  
Vol 24 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Hans Haans ◽  
Néomie Raassens ◽  
Roel van Hout
Keyword(s):  
Search Engine ◽  
Conversion Rates ◽  
The Impact

Impact of Search Engine Optimization as a Marketing Tool

2018 ◽  
Vol 7 (1) ◽  
pp. 23-36 ◽  
Author(s):  
Ravneet Singh Bhandari ◽  
Ajay Bansal
Keyword(s):  
Search Engine ◽  
Market Share ◽  
Brand Equity ◽  
Product Information ◽  
Point Of View ◽  
Product Price ◽  
Search Engine Optimization ◽  
Marketing Tool ◽  
Marketing Variables ◽  
The Impact

Today’s world revolves around information that is the driving force behind any economic value chain. The thirst for information has led to the evolution of online “Search Engines” over last few years and are the most widely used instruments currently. Gradually marketers also started using this platform for marketing their products. This study focuses on the impact of search engine optimization as a marketing tool and its influence on various marketing variables like market share, brand equity and others. Literature review highlights many marketing variables getting affected by search engine optimization. Variables like market share, brand loyalty, brand recognition, product price, product information, brand image, brand awareness, consumer online behavior, and user reviews are few of them. The authors have found that most of the researches have highlighted these variables either in isolation or may be in combination of few. Few studies have considered variables only from marketer’s point of view and others from buyer’s point of view. In this study, the authors have attempted to comprehend and understand empirically, the impact of search engine optimization on various marketing variables identified (after the study) as market share and brand equity as the most prominent ones and product awareness, purchase persuasion and consumer insights the other important ones. To analyze the said phenomenon, the initial step was the examination of the significant writing to develop a comprehension about different parameters of search engine for the brand post. The data were gathered through questionnaire from the sample of 338 respondents who were selected by simple random sampling method mostly from the National Capital Region (NCR) of Delhi in India. The data collected from the respondents were loaded on SAS base for exploratory factor analysis and multiple regression analysis.

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