Search results

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

In recent years, the World Wide Web (WWW) has become incredibly popular in homes and offices alike. Consumers need to search for relevant information to help solve purchasing problems on various Web sites. Although there is no question that great numbers of WWW users will continue using search engines for information retrieval, consumers still hesitate before making a final decision, often because only rough and limited information about the products is made available. Consequently, consumers need the help of data mining in order to help them make informed decisions. Herein we propose a new approach to integrating a search engine with data mining in an effort to help support customer‐oriented information search action. This approach also illustrates how to reduce the consumer’s information search perplexity.

The aim of this research was to examine the use of the data island method for creating metadata records based on DCXML, MARCXML, and MODS with indexability and visibility of element tag names in web search engines.

A total of 600 metadata records were developed in two groups (300 HTML-based records in an experimental group with special structure embedded in the < pre> tag of HTML based on the data island method, and 300 XML-based records as the control group with the normal structure). These records were analyzed through an experimental approach. The records of these two groups were published on two independent websites, and were submitted to Google and Bing search engines.

Findings show that all the tag names of the metadata records created based on the data island method relating to the experimental group indexed by Google and Bing were visible in the search results. But the tag names in the control group's metadata records were not indexed by the search engines. Accordingly it is possible to index and retrieve the metadata records by their tag name in the search engines. But the records of the control group are accessible by the element values only. The research suggests some patterns to the metadata creators and the end users for better indexing and retrieval.

The research used the data island method for creating the metadata records, and deals with the indexability and visibility of the metadata element tag names for the first time.

The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons.

The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform.

Google’s top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists.

The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries.

The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.

The Web is the largest repository of information. Personal information is usually scattered on various pages of different websites. Search engines have made it easier to find personal information. An attacker may collect a user's scattered information together via search engines, and infer some privacy information. The authors call this kind of privacy attack “Privacy Inference Attack via Search Engines”. The purpose of this paper is to provide a user‐side automatic detection service for detecting the privacy leakage before publishing personal information.

In this paper, the authors propose a user‐side automatic detection service. In the user‐side service, the authors construct a user information correlation (UICA) graph to model the association between user information returned by search engines. The privacy inference attack is mapped into a decision problem of searching a privacy inferring path with the maximal probability in the UICA graph and it is proved that it is a nondeterministic polynomial time (NP)‐complete problem by a two‐step reduction. A Privacy Leakage Detection Probability (PLD‐Probability) algorithm is proposed to find the privacy inferring path: it combines two significant factors which can influence the vertexes' probability in the UICA graph and uses greedy algorithm to find the privacy inferring path.

The authors reveal that privacy inferring attack via search engines is very serious in real life. In this paper, a user‐side automatic detection service is proposed to detect the risk of privacy inferring. The authors make three kinds of experiments to evaluate the seriousness of privacy leakage problem and the performance of methods proposed in this paper. The results show that the algorithm for the service is reasonable and effective.

The paper introduces a new family of privacy attacks on the Web: privacy inferring attack via search engines and presents a privacy inferring model to describe the process and principles of personal privacy inferring attack via search engines. A user‐side automatic detection service is proposed to detect the privacy inference before publishing personal information. In this user‐side service, the authors propose a Privacy Leakage Detection Probability (PLD‐Probability) algorithm. Extensive experiments show these methods are reasonable and effective.

The purpose of this paper is to clarify how many removal requests are made, how often, and who makes these requests, as well as which websites are reported to search engines so they can be removed from the search results.

Undertakes a deep analysis of more than 3.2bn removed pages from Google’s search results requested by reporting organizations from 2011 to 2018 and over 460m removed pages from Bing’s search results requested by reporting organizations from 2015 to 2017. The paper focuses on pages that belong to the .pl country coded top-level domain (ccTLD).

Although the number of requests to remove data from search results has been growing year on year, fewer URLs have been reported in recent years. Some of the requests are, however, unjustified and are rejected by teams representing the search engines. In terms of reporting copyright violations, one company in particular stands out (AudioLock.Net), accounting for 28.1 percent of all reports sent to Google (the top ten companies combined were responsible for 61.3 percent of the total number of reports).

As not every request can be published, the study is based only what is publicly available. Also, the data assigned to Poland is only based on the ccTLD domain name (.pl); other domain extensions for Polish internet users were not considered.

This is first global analysis of data from transparency reports published by search engine companies as prior research has been based on specific notices.

The study found a significant correlation between the number of external links and the journal impact factor for LIS journals. Journals with higher journal impact factor scores tend to attract more links to their Web sites. The study also investigated issues pertaining to data collection methods for webometrics research. It showed that the choice of search engine for data collection could affect the conclusion of a study. Data collected at different time periods were found to be fairly stable. The use of multiple rounds of data collection was shown to be beneficial, especially when the result from a single round of data is borderline significant or inconclusive.

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

The study is limited to only one country and to one year of data.

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

This is the first study to establish a relationship between search engine query data and business performance and position data.

The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.

The purpose of this paper is to examine two research questions: What search engine queries lead users to the Auburn University electronic theses and dissertations (AUETDs) collection? Do these queries vary for users in different locations and, if so, how?

Search engine queries used to locate the AUETDs collection were obtained from Google Analytics and were separated into groups based on user location. These queries were assigned to empirically derived categories based on their content.

Most local users’ queries contained person names, variants for thesis or dissertation, and variants for Auburn University. Over a third were queries for the AUETDs collection, while the remainder were seeking theses and dissertations from specific Auburn researchers. Most out-of-state users’ queries contained title and subject keywords and appeared to be seeking specific research studies. Queries from users located within the state but outside of the local area were intermediate between these groups.

Over two-thirds of visits to the AUETDs collection were made by search engine users which reinforces the importance of having repository content indexed by search engines such as Google. The specificity of their queries indicates that full-text indexing will be more helpful to users than metadata indexing alone.

This is the first detailed analysis of search engine queries used to locate an ETDs collection. It may also be the last, as query content for the major search engines is no longer available from Google Analytics.