Impact of search engines on page popularity

Cho, Junghoo; Roy, Sourashis

doi:10.1145/988672.988676

Cited by 223 publications

(156 citation statements)

References 20 publications

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“…This is justified in real settings by observing, for example, that sites that are highly ranked by search engines have more visibility, and therefore have a better chance to be linked from other sites, leading to even higher visibility [132]. The model can also be extended to allow for link deletions [255].…”

Section: Preferential Attachmentmentioning

confidence: 99%

“…An increasing fraction of traffic enters a website by a redirection from a search engine, rather than by browsing through the site's home page (data for blogs indicates that search engines lead to a third of all read requests, higher than any other individual source [192]). Thus pages that rank high on search engines tend to become ever more popular, whereas new pages that are not indexed have a much harder time [132].…”

Section: Page Popularitymentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Preferential Attachmentmentioning

confidence: 99%

Section: Page Popularitymentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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show abstract

“…In [12], Cho et al examined the impact of search engines on the popularity of web pages. In [13], Fortunato et al found that search engines avoided a theoretical vicious cycle of popularity, and instead had a tendency to send users to less popular pages than they would have otherwise found.…”

Section: Related Workmentioning

confidence: 99%

Navigation characteristics of online social networks and search engines users

Dunn

Gupta

Gerber

et al. 2012

Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks

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Online social networks (OSNs) represent a significant portion of Web traffic today, comparable with search engines. Even though their primary purpose is different from that of search engines, OSNs are impacting how users navigate the Web and what types of websites they visit. This paper is motivated by the desire to understand the similarities and differences in the websites users visit through OSNs versus through search engines. Using Web traffic logs from 17,000 DSL subscribers of a Tier 1 ISP in the United States, we find that while OSN visitors are less likely to navigate to external websites, when they do, they spend more time at those websites compared to when search engine users visit external websites. Also, OSNs send their users to a narrower subset of the Web than search engines. While websites related to games and video are more commonly visited from OSNs, shopping and reference sites are common for search engines. Finally, OSNs send their visitors to less popular domains more often than search engines. Our findings can be useful to ISPs in network provisioning and traffic engineering.

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“…Identification of web communities was explored in [23,12]. The effect that search engines have on page popularity was discussed in [7].…”

Section: Introductionmentioning

confidence: 99%

Web Spam, Social Propaganda and the Evolution of Search Engine Rankings

Metaxas

2010

Lecture Notes in Business Information Processing

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Summary. Search Engines have greatly influenced the way we experience the web. Since the early days of the web, users have been relying on them to get informed and make decisions. When the web was relatively small, web directories were built and maintained using human experts to screen and categorize pages according to their characteristics. By the mid 1990's, however, it was apparent that the human expert model of categorizing web pages does not scale. The first search engines appeared and they have been evolving ever since, taking over the role that web directories used to play.But what need makes a search engine evolve? Beyond the financial objectives, there is a need for quality in search results. Search engines know that the quality of their ranking will determine how successful they are. Search results, however, are not simply based on well-designed scientific principles, but they are influenced by web spammers. Web spamming, the practice of introducing artificial text and links into web pages to affect the results of web searches, has been recognized as a major search engine problem. It is also a serious users problem because they are not aware of it and they tend to confuse trusting the search engine with trusting the results of a search.In this paper, we analyze the influence that web spam has on the evolution of the search engines and we identify the strong relationship of spamming methods on the web to propagandistic techniques in society. Our analysis provides a foundation for understanding why spamming works and offers new insight on how to address it. In particular, it suggests that one could use social anti-propagandistic techniques to recognize web spam.

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Impact of search engines on page popularity

Cited by 223 publications

References 20 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Navigation characteristics of online social networks and search engines users

Web Spam, Social Propaganda and the Evolution of Search Engine Rankings

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