Search Engine Results Research Articles

DESIGNING APPLICATIONS FOR CARRYING OUT VOCATIONAL GUIDANCE WORK. SUBSITE DESIGN PROBLEMS

The article investigated the current problem of promoting the image of the university. One aspect of this may be the creation of subsites to the main site of the university. It will become the newest tool, a communication platform for entrants. A modern website is a tool, a communication technology platform, a new space for interaction between teachers and students. It's not just about reputation and public relations. This is a new practice of society in the age of informatization. The key role in the competitive confrontation of consumer needs in educational services, as well as for the worldwide recognition of universities, is assigned to its corporate website. In order for the site to take a worthy place in the rankings, it is necessary to take action to improve the internal factors of the site. Therefore, the development of both the university website and its subsites should be approached responsibly. It is necessary to pay attention at development to stylistic registration of pages (it should be executed in the same style, as the main site). Nowadays, it is also necessary to adhere to cross-browser and take into account search engine optimization. This is a set of measures that would raise the position of the site in the results of search engines for certain user queries in order to promote the site. It is also necessary to pay attention to the development of the navigation system of the subsite. An interesting solution that will distinguish this subsite from others was the use of geographic information systems to create a navigation map. The final stage of resource development should be its testing. In this way it is possible to create an effective website of the university, which will guarantee the transparency of educational and scientific activities of the university, directly and indirectly affecting its competitiveness and demand in the higher education market by the target audience, primarily entrants.

МЕТОД КЛАСИФІКАЦІЇ ЗАДАЧ ОПТИКИ ТА ЛАЗЕРНОЇ ФІЗИКИ

In recent decades, due to the use of computer technology, the results of research in a particular area of science and technique are accumulated very quickly. In the process of creating a database of optical images and knowledge base is appeared the task of classification method absence. Without such a method the creating the structure of data warehouse is difficult to solve. The classification of the tasks and the obtained results requires considerable attention in order to ensure their rapid and accurate search and further use. Classification is one of the most important tasks of large database analysis used in Data Mining. Assessment of classification accuracy can be performed using cross-checking or a test set. The proposed method is suitable for use when the accuracy of the test set classification meets the established requirements. In the field of optics and laser physics dealt with the problem, which usually is grouped by the next attributes: the initial field pattern, the type of the optical system, the characteristics of the obtained spatial distribution of light as the number of maxima, minima, zeros of intensity and other significant objects. The question of classification of the tasks themselves was considered in the paper for the first time. The method of classification of problems in optics and laser physics is developed. It is applied to the problems of diffraction, interference and observation of microobjects. The classification is carried out with the help of mathematical methods, a formal description of the objects and the decision trees method. The 5 classes was chosen: "meaning the task", "solution method", "appointment", "complexity", "way of expression." The attributes of the problem classification are "visual-optical", "optical", "computational", "experimental", "qualitative", "fundamental", "applied", "simple", "complex", "textual", "numerical", "graphical". The scheme of problems in optics and laser physics classification is shown. Decision-making to determine the attributes of the problem is carried out using the decision trees method. Application of the results in search engines of big databases and pattern recognition problems is discussed.

Toward a fairer information retrieval system

With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing , in-processing and post-processing. Pre-processing reduces bias in the data that is fed into machine learning models. In-processing encodes fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias exposed to users. This dissertation explored ways to bring the pre-processing and post-processing approaches, together with the fairness-aware evaluation metrics, into a unified framework as an attempt to break the vicious cycle of bias and improve fairness in IR. We first investigated the existing bias presented in search engine results. Specifically, we focused on the top-k fairness ranking in terms of statistical parity fairness and disparate impact fairness definitions. With Google search and a general purposed text cluster as a lens, we explored several topical diversity fairness ranking strategies to understand the relationship between relevance and fairness in search results. Our experimental results showed that different fairness ranking strategies resulted in distinct utility scores and performed differently with distinct datasets. Second, to further investigate the relationship of data and fairness algorithms, we developed a statistical framework that was able to facilitate various analysis and decision making. Our framework could effectively and efficiently estimate the domain of data and solution space. We derived theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and applied them to both synthetic datasets and real world datasets. We presented a series of use cases to demonstrate how our framework was applied to associate data and provide insights to fairness optimization problems. Third, we proposed an evaluation metric FAIR for the ranking results that encoded fairness, diversity, novelty and relevance. This metric offered a new perspective of evaluating fairness-aware ranking results. Based on this metric, we developed an effective ranking algorithm that jointly optimized for fairness and utility. Our experiments showed that our new metric was able to highlight results that achieved good user utility and fair information exposure at the same time. We showed how FAIR metric related to existing metrics through correlation analysis and case studies, and demonstrated the effectiveness of our FAIR-based algorithm.

The Implementasi Metode On-Page Search Engine Optimization untuk Meningkatkan Peringkat Website sebagai Hasil Pencarian Google

Current development of the internet world has been growing rapidly, especially in the field of website. People use search engines to find the news or information they needed on a website. One of the many indications of the success of a website is traffic. Traffic could be received from various factors, one of which is website rank in Search Engine Result Page (SERP). To improve the SERP, SEO methods are required. This research will implement SEO to website especially on the image, and then analyzed by using a tester tools, for example SEOptimer, Pingdom Tools, and SEO Site Checkup. After the website has been optimized, tested with the same tester tools. From the research results can be seen whether image optimization can affect SERP.

A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results

A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers.

Language learners' digital literacies: Focus on students' information literacy and reading practices online

AbstractThe widespread use of technology and the Internet have changed many of language learners' everyday practices, including literacies. While there have been many studies with the focus on language learners' digital literacies, few, however, have explored language learners' digital information literacy and online reading practices with the use of social bookmarking tools, especially in a community college setting. We address this gap by investigating community‐college language learners' digital literacies when social bookmarking with the focus on digital information and online reading practices from an ecological perspective. In this qualitative multiple‐case study, the focal participants were five English learners, students in an English as a Second Language writing course in a community college in the northeastern United States. Data collection included interviews, observations, and researchers' e‐journals. Thorough within‐ and cross‐case analysis of data shows that language learners searched for digital texts and evaluated them based on relevance, reliability, interest, language, and importance for them and their learning community in the social bookmarking tool. The participants struggled with the number of results in search engines, keywords, and evaluation of digital texts for relevance and reliability. We show the need for more instruction, support, and guidance of language learners' digital information literacy practices as well as the benefits of providing students with opportunities to read digital texts. Our suggestions for future research include investigating the role of multimodality and other factors that influence language learners' evaluation practices when they look for and read information online.

Understanding the characteristics of professional development programs for teachers’ health and wellbeing: Implications for research and practice

Professional development (PD) provides an opportunity to promote the psychological, social, and physical health tools teachers require to maintain teacher wellbeing. Despite their potential, little is known about PD programs targeting the health and wellbeing of Australian teachers. This study aimed to summarize the characteristics of Australian PD programs targeted at teacher wellbeing, identify gaps in existing PD and make recommendations for future research and practice. Three search strategies, (1) search engine results, (2) a manual search of known Australian education websites, and (3) requests for information from Australian education organizations, were combined to ensure a comprehensive inventory of PD programs was compiled. This study found 63 PD programs promoting health and wellbeing that currently exist for Australian teachers. Of these, only three provided evidence of their evaluation indicating programs are advertised and implemented without evidence of their effectiveness. Future PD should be evaluated with findings of the evaluations reported publicly so evidence-based programs promoting teacher’s health and wellbeing can be recommended and implemented.

Evaluation metrics for measuring bias in search engine results

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society.

The People`s (Republic) Algorithms

Recommendation Algorithms, such as those behind social media feeds and search engine results, are the prism through which we acquire information in our digital age. Critics ascribe many social and political woes—such as the prevalence of misinformation and political division—to the fact that we view our world through the personalized and atomized prism of recommendation AI. The way the great powers of the Internet –the US, the EU and China—choose to regulate recommendation algorithms will therefore have a serious impact on our lives and political well-being. On August 27th, 2021, the Cyberspace Administration of China, a governmental internet watchdog, published a bombshell draft regulation directed at recommendation algorithms. If enforced, these regulations will exponentially increase the control and autonomy of Chinese netizens over their digital life. At the same time, the proposal will also greatly increase the control the Chinese government has over these algorithms. In this timely essay, we analyze the content of the draft proposal and situate it in its historical and political context.

The Readability of Online Health Information on Middle East Respiratory Syndrome Coronavirus Disease

Objectives: The study aims at exploring the readability of health websites on Middle East respiratory syndrome coronavirus (MERS‐CoV). Methods: The term was searched in Google, Yahoo, and Bing search engines. The readability of the first 30 results for each search engine was evaluated by using the five readability scales, including Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), Simple Measure of Gobbledygook (SMOG), Coleman-Liau Index (CLI) and Gunning Fog. Moreover, the official HONcode toolbar was used to identify websites that had been officially certified by the HON foundation. Results: Almost half of the retrieved websites were governmental (44.2%). All the surveyed websites were written above the recommended level and so, their readability is suitable for those with a high school or a college degree. The mean grade level for the MERS related websites was in a similar range across the five readability scales. Furthermore, there was no association between the search rank, credibility, and readability Discussion: The readability level of MERS information available through search engines results, exceeds the recommended 6th-grade level, and they do not currently adhere to the recommended readability guidelines. Even credible websites have provided the contents that are not readable enough for the public. Conclusion: Considering the lack of a specific policy about the providing of readable health information on the web, it is recommended for healthcare providers to advise their patients to use the online information after consulting with the physicians.

Retrieval Evaluation Measures that Agree with Users’ SERP Preferences

We examine the “goodness” of ranked retrieval evaluation measures in terms of how well they align with users’ Search Engine Result Page (SERP) preferences for web search. The SERP preferences cover 1,127 topic-SERP-SERP triplets extracted from the NTCIR-9 INTENT task, reflecting the views of 15 different assessors. Each assessor made two SERP preference judgements for each triplet: one in terms of relevance and the other in terms of diversity. For each evaluation measure, we compute the Agreement Rate (AR) of each triplet: the proportion of assessors that agree with the measure’s SERP preference. We then compare the mean ARs of the measures as well as those of best/median/worst assessors using Tukey HSD tests. Our first experiment compares traditional ranked retrieval measures based on the SERP relevance preferences: we find that normalised Discounted Cumulative Gain (nDCG) and intentwise Rank-biased Utility (iRBU) perform best in that they are the only measures that are statistically indistinguishable from our best assessor; nDCG also statistically significantly outperforms our median assessor. Our second experiment utilises 119,646 document preferences that we collected for a subset of the above topic-SERP-SERP triplets (containing 894 triplets) to compare preference-based evaluation measures as well as traditional ones. Again, we evaluate them based on the SERP relevance preferences. The results suggest that measures such as wpref5 are the most promising among the preference-based measures considered, although they underperform the best traditional measures such as nDCG on average. Our third experiment compares diversified search measures based on the SERP diversity preferences as well as the SERP relevance preferences, and it shows that D♯-measures are clearly the most reliable: in particular, D♯-nDCG and D♯-RBP statistically significantly outperform the median assessor and all intent-aware measures; they also outperform the recently proposed RBU on average. Also, in terms of agreement with SERP diversity preferences, D♯-nDCG statistically significantly outperforms RBU. Hence, if IR researchers want to use evaluation measures that align well with users’ SERP preferences, then we recommend nDCG and iRBU for traditional search, and D♯-measures such as D♯-nDCG for diversified search. As for document preference-based measures that we have examined, we do not have a strong reason to recommended them over traditional measures like nDCG, since they align slightly less well with users’ SERP preferences despite their quadratic assessment cost.

Assessing Readability of Patient Education Materials on Breast Reconstruction by Major US Academic Hospitals as Compared With Nonacademic Sites.

Women undergoing postmastectomy breast reconstruction face many complex choices. A myriad of online patient education materials exist to assist in decision making. Understanding of these materials, termed health literacy, affects surgical decision making and outcomes. The National Institutes of Health recommends writing patient education materials at a sixth- to seventh-grade reading level. The primary goal of this study was to assess readability of online breast reconstruction materials. Resources were collected from every academic hospital with a plastic surgery residency program, 97 in total. These data were compared with the top nonacademic Web sites ranked by search engine results, 14 in total. Materials were analyzed using 3 validated readability assessment scales: Coleman-Liau Index, SMOG (Simplified Measure of Gobbledygook) Readability Formula, and Flesch-Kincaid Grade Level. Average readability was analyzed, and results were compared using a 1-way analysis of variance to assess for significance between the different tools and a 2-sided t test to assess for significance between academic and nonacademic readability results. The mean readability scores across the academic programs were a Coleman-Liau Index of 13.38 (standard Deviation [SD] 2.81, 13th to 14th grade), Flesch-Kincaid Grade Level of 13.04 (SD = 3.9, 13th grade), and SMOG Readability of 13.64 (SD = 2.99, 13th to 14th grade). For the 14 nonacademic sites, results showed a Coleman-Liau Index of 11.93 (SD = 1.14, 12th grade), Flesch-Kincaid of 11.82 (SD = 2.33, 11th to 12th grade), and SMOG Readability of 10.91 (SD = 1.79, 11th grade). One-way analysis of variance demonstrated no significant differences in mean readability scores across the 3 readability tools used (academic F = 2.7804, P = 0.06; nonacademic F = 1.14, P = 0.33). Two-tailed t test results demonstrated that there was statistical significance between readability of the academic in comparison with nonacademic search engine results (t = 2.04, P = 0.04). Average readability across all Web sites were much higher than the recommended reading level, at a 13th to 14th grade reading level for academic institutions, and an 11th to 12th grade for nonacademic Web sites. Plastic surgeons may contribute to improving patient understanding and perioperative outcomes through revising patient education materials.

Experimental realization of continuous-time quantum walks on directed graphs and their application in PageRank

PageRank is an algorithm used by Google Search to rank web pages in their search engine results. An important step for quantum networks is to quantize the classical protocol as quantum mechanics provides computational resources that can be used to outperform classical algorithms. In this paper, we experimentally realize continuous-time quantum walks for directed graphs with non-Hermitian adjacency matrices by using linear optical circuits and single photons. We find that the node classical centrality in a directed graph is correlated with the maximum node probability resulting from a continuous-time quantum walk and then demonstrate PageRank. Our work opens up an avenue of applications of quantum information in real-life tasks.

The Web’s Potential to Provide Depression Literacy Resources to Latinx Teens: A Missed Opportunity?

Among U.S. adolescents, Latinxs have the highest levels of depression and the lowest access to treatment. Educational resources on the World Wide Web have potential to redress disparities by improving mental health literacy, but little is known about the availability and quality of web documents about depression targeting Latinx teens. We conducted a qualitative content analysis of search-engine results to inform future research. Of the 330 results, the majority (48%) were news stories, most with breaking news in Spanish about youth suicides. Only two documents targeted Latinx youth, including one that was depression-specific. A critique of this document revealed that although it met some indicators of web-resource quality, it did not meet its potential to empower Latinx teens to raise their depression literacy. We argue that such scarcity is an access barrier to preventive care and contributes to existing disparities. We highlight the need for web documents aiming to enhance depression literacy among Latinx teens that are both easily discoverable as well as socio-politically and culturally tailored.

Characteristics of online pharmacies selling Adderall

ObjectivesAdderall (amphetamine-dextroamphetamine) is a controlled substance with harmful adverse effects if abused or misused. We assessed the availability of Adderall from common search engines, and evaluated the safety and marketing characteristics of online pharmacies selling Adderall. DesignCross-sectional study. Setting and participantsFrom December 2019 to February 2020, the phrase “buy Adderall online” was queried in four search engines: Google (N = 100), Bing (N = 100), Yahoo (N = 50) and DuckDuckGo (N = 50). Online pharmacies that claimed to sell Adderall and had unique Uniform Resource Locators, were active, free-access, and in English language were included. Outcome measuresOnline pharmacies were categorized as rogue, unclassified, or legitimate on the basis of LegitScript classifications. Safety and marketing characteristics, and costs were collected. ResultsOf the 62 online pharmacies found to sell Adderall, 61 were rogue or unclassified. Across all rogue and unclassified online pharmacies, prescriptions were not required (100%), pharmacist services were not offered (100%), and quantity limits were not placed on the number of Adderall purchases (100%). Rogue and unclassified online pharmacies appealed to cost, offering price discounts (61%), bulk discounts (67%), and coupon codes (70%). Contrary to their claims, cheaper prices were available for all formulations and dosages of Adderall from GoodRx than from these online pharmacies. Rogue and unclassified online pharmacies promoted and enabled the illicit purchase of Adderall, appealing to privacy (74%), offering purchase through cryptocurrency (74%), and claiming registration or accreditation of their sites (33%). ConclusionRogue online pharmacies are pervasive in search engine results, enabling the illicit purchase of Adderall without a prescription. Consumers are at risk of purchasing Adderall, a medication with high abuse potential, from unsafe sources. Law enforcement, regulatory agencies, and search engines should work to further protect consumers from unregistered and illegitimate online pharmacies selling Adderall.

An Algorithm for Multi-Domain Website Classification

The web is the largest world-wide communication system of computers. The web has local, academic, commercial and government sites. As the types of websites increases in numbers, the cost and accuracy of manual classification became cumbersome and cannot satisfy the increasing internet service demands, thereby automated classification became important for better and more accurate search engine results. Therefore, this research has proposed an algorithm for classifying different websites automatically by using randomly collected textual data from the webpages. This research also contributed ten dictionaries covering different domains and used as training data in the classification process. Finally, the classification was carried out using the proposed and Naïve Bayes algorithms and found the proposed algorithm outperformed on the scale of accuracy by 1.25%. This research suggests that the proposed algorithm could be applied to any number of domains if the related dictionaries are available.

Meta Search Engine using Semantic Similarity and Correlation Coefficient

This paper aims to provide an intelligent way to query and rank the results of a Meta Search Engine. A Meta Search Engine takes input from the user and produces results which are gathered from other search engines. The main advantage of a Meta Search Engine over methodical search engine is its ability to extend the search space and allows more resources for the user. The semantic intelligent queries will be fetching the results from different search engines and the responses will be fed into our ranking algorithm. Ranking of the search results is the other important aspect of Meta search engines. When a user searches a query, there are number of results retrieved from different search engines, but only several results are relevant to user's interest and others are not much relevant. Hence, it is important to rank results according to the relevancy with user query. The proposed paper uses intelligent query and ranking algorithms in order to provide intelligent meta search engine with semantic understanding.

Region-action LSTM for mouse interaction sequence based search satisfaction evaluation

Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper, we leverage regions together with actions to extract sequences from mouse interaction data. Using regions to capture the spatial information in mouse interaction data would reserve more details of the interaction processes between users and SERPs. To modeling mouse interaction sequences for search satisfaction evaluation, we propose a novel LSTM unit called Region-Action LSTM (RALSTM), which could capture the interactive relations between regions and actions without subjecting the network to higher training complexity. Simultaneously, we propose a data augmentation strategy Multi-Factor Perturbation (MFP) to increase the pattern variations on mouse interaction sequences. We evaluate the proposed approach on open datasets. The experimental results show that the proposed approach achieves significant performance improvement compared with the state-of-the-art search satisfaction evaluation approach.

The new trend for search engine optimization, tools and techniques

<span>Search Engines are used to search any information on the internet. <br /> The primary objective of any website owner is to list their website at the top of all the results in Search Engine Results Pages (SERPs). Search Engine Optimization is the art of increasing visibility of a website in Search Engine Result Pages. This art of improving the visibility of website requires the tools and techniques; This paper is a comprehensive survey of how a Search Engine (SE) works, types and parts of Search Engine and different techniques and tools used for Search Engine Optimization (SEO.) In this paper, we will discuss the current tools and techniques in practice for Search Engine Optimization.</span>

If You Host It Online, Who Will (and Will Not) Come? Individual and Partner Enrollment in a Web-Based Intervention for Distressed Couples.

Given the increasing utilization of online recruitment and delivery for prevention programming, the current study was designed to examine the ways in which recruitment and eligibility factors affect the resulting size and composition of participants in an online intervention. Study hypotheses were tested from a sample of 2512 low-income individuals who sought to enroll in OurRelationship, a web-based intervention for distressed couples. Results indicated that more than half of the sample (62%) learned about the OurRelationship program from results of an online search engine. Differences in participant characteristics were observed on the basis of recruitment source, with individuals recruited from an online search and from social media being characterized by higher levels of relationship distress and personal psychological distress relative to those who learned about the program through other means. Partner participation requirements also had a significant effect on the final sample of participants, as more than half of help-seeking individuals (52%) had partners who did not complete the screening enrollment form and were thus ineligible to receive services. Furthermore, compared with individuals whose partners completed the enrollment form, individuals whose partners did not participate were characterized by greater levels of break-up potential, physical aggression, communication conflict, psychological distress, and anger. Findings from the study suggest that some, but not all, online sources recruit more at-risk populations as well as illustrate the ways in which partner participation requirements can screen out interested individuals that appear in most need of services. Implications for prevention researchers and practitioners are discussed.