Bing Search Engine Research Articles

Web scraping of user-simulated online nutrition information for people with multiple sclerosis

BackgroundPeople diagnosed with multiple sclerosis (MS) often seek to modify their diet guided by online advice, however this advice may not align with national dietary guidelines. The aim of this study was to simulate an online search for dietary advice conducted by a person with MS and evaluate the content. It was hypothesised that a variety of eating patterns are promoted for MS online and these dietary approaches can be contradictory. MethodsAn online search was simulated using Google Trends-informed search terms and Google and Bing search engines. URLs were extracted using R. Nutrition data were extracted including recommendations for diets, foods, supplements, and health professional consultation. Statistical analyses were conducted using R. Results73 URLs from 49 websites were extracted, with only 14 results common to both search engines. Dietary recommendations included overall eating patterns (58 webpages, 79%), individual foods (55 webpages, 75%), and supplements (33 webpages, 45%). The most promoted eating pattern for MS was a balanced diet (33 recommendations, 48%), more likely by nonprofit organisations and health information websites (14 and 17 recommendations, 100% and 89%); lifestyle program websites were more likely to recommend restrictive diets (19 recommendations, 100%) (p<0.001). 52% pages advised consulting a health professional, most often a doctor or dietitian. ConclusionA balanced diet is the most recommended eating pattern for MS online, though advice promoting restrictive diets persists.

Quality, Reliability, and Readability of Online Information on Idiopathic Intracranial Hypertension.

To evaluate the quality & reliability, technical quality, and readability of patient-targeted online information on idiopathic intracranial hypertension. In this cross-sectional study, we searched Google and Bing search engines for the terms "idiopathic intracranial hypertension" and "pseudotumor cerebri." We evaluated the first 50 search outcomes for each search term. Peer-reviewed articles, targeted advertisements, book chapters, videos, personal blogs, websites intended for healthcare professionals, and non-English websites were excluded. Websites were classified as institutional or private. Two independent reviewers assessed each website for 1) quality and reliability using DISCERN, Health on the Net Code of Conduct (HONcode), and JAMA criteria and 2) technical quality based on 11 criteria. Readability was evaluated using 6 measures (Flesch-Kincaid Reading Ease score and grade level, Gunning Fog, Simple Measure of Gobbledygook (Smog) index, Coleman-Liau index, and automated readability index). Sixty-three websites (37 institutional) were included. The mean scores for the DISCERN, HONcode, and JAMA criteria were 3.6 ± 1 (1-4.8; 1: worse, 5: best), 10.3 ± 2.8 (2-16; 0: worse, 16: best), and 2 ± 1.3 (0-4; 0: worse, 4: best), respectively. The mean technical quality score was 0.8 ± 0.1 (0.5-1). The mean Flesch-Kincaid grade level score was 8.9 ± 1.8 (3.3-13.3). For Flesch-Kincaid grade level, 47 (74.6%) websites scored a grade level of 10 or less. For Gunning Fog Score, 35 websites (55.6%) scored from 7.3 to 10.8. For the Smog Index, 46 (73%) websites scored 5.7-8. The mean Coleman Liau Index was 16 ± 2.1 (9.6-22.2). For the automated readability index, 30 (50.7%) websites scored less than the eighth grade. No statistically significant difference was present between institutional and private websites except for JAMA, which scored higher in private websites (2.4 vs 1.7, P = 0.03). Our findings suggest that online information on idiopathic intracranial hypertension/pseudotumor cerebri for patients generally demonstrates moderate to good quality and reliability. Although most websites maintain credible technical quality, the readability exceeds recommended levels for the average American reader. This may impede understanding, emphasizing the need for future improvements.

Beyond the Hype-The Actual Role and Risks of AI in Today's Medical Practice: Comparative-Approach Study.

The evolution of artificial intelligence (AI) has significantly impacted various sectors, with health care witnessing some of its most groundbreaking contributions. Contemporary models, such as ChatGPT-4 and Microsoft Bing, have showcased capabilities beyond just generating text, aiding in complex tasks like literature searches and refining web-based queries. This study explores a compelling query: can AI author an academic paper independently? Our assessment focuses on four core dimensions: relevance (to ensure that AI's response directly addresses the prompt), accuracy (to ascertain that AI's information is both factually correct and current), clarity (to examine AI's ability to present coherent and logical ideas), and tone and style (to evaluate whether AI can align with the formality expected in academic writings). Additionally, we will consider the ethical implications and practicality of integrating AI into academic writing. To assess the capabilities of ChatGPT-4 and Microsoft Bing in the context of academic paper assistance in general practice, we used a systematic approach. ChatGPT-4, an advanced AI language model by Open AI, excels in generating human-like text and adapting responses based on user interactions, though it has a knowledge cut-off in September 2021. Microsoft Bing's AI chatbot facilitates user navigation on the Bing search engine, offering tailored search. In terms of relevance, ChatGPT-4 delved deeply into AI's health care role, citing academic sources and discussing diverse applications and concerns, while Microsoft Bing provided a concise, less detailed overview. In terms of accuracy, ChatGPT-4 correctly cited 72% (23/32) of its peer-reviewed articles but included some nonexistent references. Microsoft Bing's accuracy stood at 46% (6/13), supplemented by relevant non-peer-reviewed articles. In terms of clarity, both models conveyed clear, coherent text. ChatGPT-4 was particularly adept at detailing technical concepts, while Microsoft Bing was more general. In terms of tone, both models maintained an academic tone, but ChatGPT-4 exhibited superior depth and breadth in content delivery. Comparing ChatGPT-4 and Microsoft Bing for academic assistance revealed strengths and limitations. ChatGPT-4 excels in depth and relevance but falters in citation accuracy. Microsoft Bing is concise but lacks robust detail. Though both models have potential, neither can independently handle comprehensive academic tasks. As AI evolves, combining ChatGPT-4's depth with Microsoft Bing's up-to-date referencing could optimize academic support. Researchers should critically assess AI outputs to maintain academic credibility.

Artificial Intelligence as a Disinformation Tool: Analyzing News Photos on Climate Change in The Example of Bing Search Engine

İklim değişikliği, günümüzün en büyük ve acil sorunlarından biridir. İnsanı ve birçok türü tehdit eden bu krizde, iklim haberlerinin önemi büyüktür. Ancak son yıllarda yapay zekânın her alana yayılmasıyla birlikte dijital medyada yer alan ve arama motorlarının optimizasyonuyla bir araya getirilen haber fotoğraflarının bu konuda yetersiz kaldığı ve daha çok çeşitli tasarım programlarıyla oluşturulan tek tip ve benzer görsellerin yer aldığı gözlemlenmektedir. Yapay zekâ, filtreleme yapmadan sayısal tabanda bulunan tüm verileri bir araya toplaması nedeniyle bilgi kirliliğine neden olmakta ve iklim gibi spesifik konularda da dezenformasyon risklerine yol açmaktadır. Bu durum, kamuoyunun doğru bilgilendirilmesini zorlaştırmaktadır. Sosyal bilimlerde nitel bir araştırma yöntemi olan doküman analizinin kullanıldığı çalışmanın örneklemini, Similarweb’in, Temmuz 2022 ve Haziran 2023 arasında gerçekleştirdiği analizler sonucunda en çok kullanılan arama motorları arasında ikinci sırada yer alan ve Şubat 2023 itibarıyla yapay zekayâ entegre edilen “Bing” arama motoru oluşturmaktadır. Örneklem dâhilinde bulunan yapay zekâ destekli arama motorunun sohbet kısmına “iklim değişikliği ile ilgili haber fotoğrafları” anahtar kelimeleri yazılarak ‘görseller’ sekmesinde yer alan görseller baz alınıp çalışma alanı sınırlandırılmıştır. Çalışma kapsamında bulgulara ulaşmak için amaçlı örnekleme yöntemleri içerisinde yer alan ‘maksimum çeşitlilik örneklemesi’ kullanılmış, iklim değişikliğinin ana kavramlarından olan kuraklık, buzulların erimesi ve aşırı ısınma ile üç kategori oluşturulmuştur. Belirlenen her bir kategori için 10 adet haber fotoğrafı incelenmiştir. Elde edilen veriler doğrultusunda yapay zekânın haber fotoğraflarını ve görsellerini ayırt etmeden bir araya toplamasının, habercilikte ‘haber fotoğrafçılığı’ olarak adlandırılan kavramı zedeleyerek dezenformasyona yol açtığı gözlemlenmiştir.

Bing Chat: The Future of Search Engines?

ABSTRACTIntroduced by Microsoft in February 2023, Bing Chat is a feature of the Bing search engine that integrates an OpenAI large language model (LLM) customised for search (Mehdi, 2023a). This poster compares the outputs of Bing Chat and a standard existing search engine (DuckDuckGo) in response to identical keyword queries and corresponding natural language (NL) questions. Specifically, we examined: (1) the length of Bing Chat's responses and DuckDuckGo's first page of search results, by number of website links; and, (2) the length of Bing Chat's textual summaries, by number of website links. We found that, on average, significantly fewer websites were linked to in Bing Chat's responses compared to DuckDuckGo's search results. Our findings have important implications for website operators, who may receive less traffic and ad revenue if LLM‐enabled search engines are widely adopted in the future. Human‐Computer Interaction (HCI) will inevitably face the need for more research on human information behaviours adaptations in response to the changing search paradigm.

The Readability Analysis of Online Education Materials for Kidney Transplantation

Introduction: The growing use of online information influences people's healthcare decision making in terms of treatment or consulting a doctor. The readability of a website is a factor that influences the correct understanding of its content. Regarding that there is little information about the readability as well as the credibility of health websites in the field of kidney transplantation, the present study assesses the readability and trustworthiness of websites in this topic.Material and Methods: Google, Yahoo, and Bing search engines were used to search for "Kidney Transplantation" and "Renal Transplantation.". Four readability scales were applied to assess the readability of the first 30 results of each search engine. The HONcode toolbar was applied to recognize credible websites. The relationship between HONcode verification and website position on the search results pages was explored. Furthermore, the difference between the readability scores and website position on the search results pages was tested. The readability difference between search result pages was also examined.Results: According to the results, the assessed websites are suitable for students or high school graduates. Furthermore, the association between the average readability of websites and website position on the search results pages was significant (p-value&lt;0.05). A significant association between HONcode-verified sites and website position on the search results pages was also revealed (p-value=0.011).Conclusion: The readability of kidney transplantation websites is far above the recommended level. Therefore, health organizations must consider readability while designing their websites.

Less reliable media drive interest in anti-vaccine information

As progress on vaccine rollout in the United States slowed down in Spring 2021, it became clear that anti-vaccine information posed a public health threat. Using text data from 5,613 distinct COVID misinformation stories and 70 anti-vaccination Facebook groups, we tracked highly salient keywords regarding anti-vaccine discourse across Twitter, thousands of news websites, and the Google and Bing search engines from May through June 2021, a key period when progress on vaccinations very clearly stalled. Granger causality tests showed that searches for anti-vaccination terms on Google as well as the appearance of these terms on Twitter followed spikes in their appearance in less reliable media sites, but not discussion in the mainstream press.

How AI Responds to Common Lung Cancer Questions: ChatGPT versus Google Bard

Background The recent release of large language models (LLMs) for public use, such as ChatGPT and Google Bard, has opened up a multitude of potential benefits as well as challenges. Purpose To evaluate and compare the accuracy and consistency of responses generated by publicly available ChatGPT-3.5 and Google Bard to non-expert questions related to lung cancer prevention, screening, and terminology commonly used in radiology reports based on the recommendation of Lung Imaging Reporting and Data System (Lung-RADS) v2022 from American College of Radiology and Fleischner society. Materials and Methods Forty of the exact same questions were created and presented to ChatGPT-3.5 and Google Bard experimental version as well as Bing and Google search engines by three different authors of this paper. Each answer was reviewed by two radiologists for accuracy. Responses were scored as correct, partially correct, incorrect, or unanswered. Consistency was also evaluated among the answers. Here, consistency was defined as the agreement between the three answers provided by ChatGPT-3.5, Google Bard experimental version, Bing, and Google search engines regardless of whether the concept conveyed was correct or incorrect. The accuracy among different tools were evaluated using Stata. Results ChatGPT-3.5 answered 120 questions with 85 (70.8%) correct, 14 (11.7%) partially correct, and 21 (17.5%) incorrect. Google Bard did not answer 23 (19.1%) questions. Among the 97 questions answered by Google Bard, 62 (51.7%) were correct, 11 (9.2%) were partially correct, and 24 (20%) were incorrect. Bing answered 120 questions with 74 (61.7%) correct, 13 (10.8%) partially correct, and 33 (27.5%) incorrect. Google search engine answered 120 questions with 66 (55%) correct, 27 (22.5%) partially correct, and 27 (22.5%) incorrect. The ChatGPT-3.5 is more likely to provide correct or partially answer than Google Bard, approximately by 1.5 folds (OR = 1.55, P = 0.004). ChatGPT-3.5 and Google search engine were more likely to be consistent than Google Bard by approximately 7 and 29 folds (OR = 6.65, P = 0.002 for ChatGPT and OR = 28.83, P = 0.002 for Google search engine, respectively). Conclusion Although ChatGPT-3.5 had a higher accuracy in comparison with the other tools, neither ChatGPT nor Google Bard, Bing and Google search engines answered all questions correctly and with 100% consistency.

Losing your head? An evaluation of the readability and reliability of patient information available online for avascular necrosis of the head of the femur

TitleLosing your head? An evaluation of the readability and reliability of patient information available online for Avascular Necrosis of the Head of Femur. BackgroundAvascular necrosis of the head of femur commonly affects patients with an average age of 58.3 years, and is generally managed in the elective setting, allowing patients a timeframe to research their diagnosis and management options. The aim of this study is to evaluate the readability and reliability of the information available online to patients about this condition. MethodsGoogle, Bing and Yahoo internet search engines were utilised, using the search terms “Avascular necrosis head of femur” and “hip avascular necrosis”, with the first 30 URLs selected for analysis. Readability was assessed using an online readability calculator to produce 3 scores (Gunning FOG, Flesch Kincaid Grade and Flesch Reading Ease). Information quality was assessed using a HONcode detection web-extension and the JAMA benchmark criteria. Results86 webpages were identified for inclusion for assessment. ConclusionThe majority of the information available online about avascular necrosis of the head of the femur is not at an appropriate reading level for the general population, and less than 20% of the most accessible information available online is accredited to be of sufficient quality to be providing advice to patients. Medical professionals must work together to improve health literacy among the patients encountered, and ensure recommendation of only reliable and accessible sources of information should patients ask for guidance on finding these resources.

Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation

Expanding digital data sources, including social media, online news articles and blogs, provide an opportunity to understand better the context and intensity of human-nature interactions, such as wildlife exploitation. However, online searches encompassing large taxonomic groups can generate vast datasets, which can be overwhelming to filter for relevant content without the use of automated tools. The variety of machine learning models available to researchers, and the need for manually labelled training data with an even balance of labels, can make applying these tools challenging. Here, we implement and evaluate a hierarchical text classification pipeline which brings together three binary classification tasks with increasingly specific relevancy criteria. Crucially, the hierarchical approach facilitates the filtering and structuring of a large dataset, of which relevant sources make up a small proportion. Using this pipeline, we also investigate how the accuracy with which text classifiers identify relevant and irrelevant texts is influenced by the use of different models, training datasets, and the classification task. To evaluate our methods, we collected data from Facebook, Twitter, Google and Bing search engines, with the aim of identifying sources documenting the hunting and persecution of bats (Chiroptera). Overall, the ‘state-of-the-art’ transformer-based models were able to identify relevant texts with an average accuracy of 90%, with some classifiers achieving accuracy of >95%. Whilst this demonstrates that application of more advanced models can lead to improved accuracy, comparable performance was achieved by simpler models when applied to longer documents and less ambiguous classification tasks. Hence, the benefits from using more computationally expensive models are dependent on the classification context. We also found that stratification of training data, according to the presence of key search terms, improved classification accuracy for less frequent topics within datasets, and therefore improves the applicability of classifiers to future data collection. Overall, whilst our findings reinforce the usefulness of automated tools for facilitating online analyses in conservation and ecology, they also highlight that the effectiveness and appropriateness of such tools is determined by the nature and volume of data collected, the complexity of the classification task, and the computational resources available to researchers.

Robotic colorectal surgery: quality assessment of patient information available on the internet using webscraping

The primary goal of this study is to assess current patient information available on the internet concerning robotic colorectal surgery. Acquiring this information will aid in patients understanding of robotic colorectal surgery. Data was acquired through a web-scraping algorithm. The algorithm used two Python packages: Beautiful Soup and Selenium. The long-chain keywords incorporated into Google, Bing and Yahoo search engines were ‘Da Vinci Colon-Rectal Surgery’, ‘Colorectal Robotic Surgery’ and ‘Robotic Bowel Surgery’. 207 websites resulted, were sorted and evaluated according to the ensuring quality information for patients (EQIP) score. Of the 207 websites visited, 49 belonged to the subgroup of hospital websites (23.6%), 46 to medical centers (22.2%), 45 to practitioners (21.7%), 42 to health care systems (20,2%), 11 to news services (5.3%), 7 to web portals (3.3%), 5 to industry (2.4%), and 2 to patient groups (0.9%). Only 52 of the 207 websites received a high rating. The quality of available information on the internet concerning robotic colorectal surgery is low. The majority of information was inaccurate. Medical facilities involved in robotic colorectal surgery, robotic bowel surgery and related robotic procedures should develop websites with credible information to guide patient decisions.

Characterization of online support group resources for patients with dermatologic conditions.

Dermatologic conditions can have significant quality of life effects on patients. The internet is a first-line accessible resource for patients to seek support and community in managing dermatologic diagnoses. The accessibility and content of online support resources for patients with dermatologic conditions is unclear so we sought to characterize these resources. We conducted online searches utilizing incognito Google, Yahoo, and Bing search engines and identified a total of 36 support group resources. 9 links were for single dermatology support groups and 27 links were for databases of support groups for different dermatologic conditions. We tallied number totals and percentages of online support resources and found wide variability of material in terms of the readability of the group websites, as well as content, medium, and hosts of the groups. Furthermore, we observed an imbalance in representation of resources for certain dermatologic conditions as opposed to others, further highlighting the strong need for the creation of easy-to-access support groups for patients across the spectrum of dermatological disease.

Quality of information regarding abnormal uterine bleeding available online

IntroductionThe Internet and electronic devices with Internet access allow for a greater fluidity of information and speed of communication, especially in the field of health. Abnormal uterine bleeding (AUB) affects approximately 3–30% of women and can negatively impact their health and quality of life. Information regarding AUB that is available on the Internet may not be clear or accurate, rendering it difficult to understand and likely to result in delayed medical evaluation, which subsequently leads to worsening of the AUB. ObjectiveTo evaluate the quality of the information regarding AUB currently available on the Internet, including information regarding treatments. MethodsThe Google Trends website was searched for the most widely used English terms related to AUB. The identified descriptors were searched individually on the Google, Yahoo!, and Bing search engines. The first 10 results of each search were pre-selected and evaluated for inclusion in this study. Selected websites were categorically divided into two groups (news/magazine and academic) and individually analyzed by three experts using the DISCERN quality criteria (reliability, general quality, and quality of information) and the presence or absence of the Health on the Net Foundation Code of Conduct (HONcode®) seal. ResultsOf the 168 websites included in this study, 60.1% were allocated to the news/magazine group and 39.9% were allocated to the academic group. Over half of the websites (54.2%) did not have the HONcode® quality seal. Websites in the academic group were more likely to include accurate information regarding AUB with greater reliability than websites in the news/magazine group. There were no statistical differences regarding the general quality of the websites. Most websites were rated as either moderate quality (70.8%) or low quality (28.6%). The HONcode® criterion was found to be a confounding factor of the analyses, as the grouping and quality results of websites without this seal were significantly associated. In addition, websites in the news/magazines group were 6.7 times more likely to provide low quality information than websites in the academic group (odds ratio: 6.7; 95% confidence interval: 2.1–21.4). ConclusionThe information regarding AUB that is available on the Internet is of low to moderate quality. Academic websites present more reliable information of greater quality. The presence of the HONcode® seal is considered important to determine the quality of the content of a website, especially for news/magazine websites, and may help Internet users identify websites that contain more reliable information. Algorithms and applications that categorize the quality of information and the reliability of health content may be useful tools that can help patients clarify their symptoms for several conditions including AUB.

Evaluation of accuracy, reliability, quality, and readability of online patient information materials on coccyx injury.

The aim of this research is to evaluate the websites containing "coccydynia, coccyx trauma or fracture" in terms of readability, reliability, accuracy, and quality. Searches for "coccydynia, coccyx trauma, coccyx fracture" were carried out in the 3 most used search engines in the USA: Google, Yahoo, and Bing in February 2022. A total of 141 websites were rated by 2 different neurosurgeons for the "Global Quality Score" and "Alexa Popularity Rank." 97.2% of the sites examined include the definition of the disease, 66% include the importance of the disease, 92.9% include the symptoms of the disease, 88.7% include the treatment of the disease, 78% include the signs of the disease, 77.3% include the mechanism of the disease It has been determined that the websites examined within the scope of the research have high global quality score (GQS) and APR and are enriched with images to a large extent.

An evaluation of the suitability, readability, quality, and usefulness of online resources for patients with cancer receiving immunotherapy

ObjectiveTo evaluate the suitability, readability, quality, and usefulness of available online resources about the side effects of immunotherapy. MethodsResources were identified through online searches (Google, Microsoft Bing, and Yahoo search engines) and evaluated using the Suitability Assessment Materials (SAM), three readability formulae, the DISCERN tool (quality), and a checklist of symptom management strategies and self-management skills (usefulness). Hierarchical cluster analysis identified resources scoring highly for suitability and quality. Factors associated with the clusters were identified using Fischer’s exact tests. ResultsThe 51 resources retained for evaluation were of low suitability and quality and exceeded recommended reading levels. Resources were lacking key features to support learning (e.g., illustrations) as well as information about the risks of treatments and the effect of treatments on quality of life. Few resources addressed key self-management skills of problem-solving and taking action. The cluster analysis identified “low” and “moderate” clusters for suitability and quality, and pamphlets and booklets were more likely to be in the moderate cluster as compared to webpages. ConclusionThere is a lack of high-quality resources to support patients’ learning to self-manage side effects of immunotherapy. Practice ImplicationsResults identify the best available online resources for self-managing side effects of immunotherapy and gaps for future work. Based on findings, a template for patient education materials was developed.

Quality and readability of online information about root canal treatment and its outcomes: an infodemiological study

Root canal treatment, also known as endodontic treatment, is a common dental procedure that involves the removal of inflamed or infected pulp tissue, followed by cleaning, disinfecting, filling, and sealing the canals. The aim of this study is to categorize the content and assess the quality and readability of the online information regarding root canal treatment and related outcomes. An online search using three different terms regarding the root canal treatment and related outcomes was undertaken using Google, Yahoo, and Bing search engines. The first 100 websites that appeared in each search were analyzed. Data recorded included DISCERN instrument scores, Journal of the American Medical Association (JAMA) benchmarks, and the presence of the health on the net seal (HON). Flesh reading ease scores, flesh-kincaid grade level, the simplified measure of Gobbledygook index, and the Coleman-Liau index were calculated to assess readability. Eighty-five of the first websites remained for analysis after applying appropriate exclusion criteria. The mean overall DISCERN score was 2.91 (+1.12). Only one website (1.17%) achieved all four JAMA benchmarks. Only 12 websites (14.1%) displayed the HON seal. The reading level varied from standard to difficult among the majority of websites. Based on this study, the online information regarding root canal treatment and related outcomes has challenging readability with content of poor quality. Further efforts should be made to create and maintain high-quality, reliable, and easily readable online information sources related to root canal treatment outcomes.

Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers.

Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States

Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R2 = 0.42, p < 5.0 × 10−6). The results suggested that online screening was more prominent in underserved rural communities, therefore presenting as important tools with which to bridge mental health-care gaps in rural, resource-deficient areas.

Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire.

Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists. We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

Functional endoscopic sinus surgery: assessing the readability and quality of online information.

The aim of this study was to assess the readability and quality of online information on functional endoscopic sinus surgery (FESS). The term 'functional endoscopic sinus surgery' was entered into the Google, Bing and Yahoo search engines. The first 30 websites for each search engine were screened. Readability was assessed using the Flesch-Kincaid Reading Ease Score (FRES), Flesch-Kincaid Grade (FKG), Simple Measure of Gobbledygook (SMOG) Index, and Gunning Fog Index (GFI). Quality was assessed using the DISCERN instrument. Spearman's correlation between quality and readability was calculated. Thirty-three websites met the inclusion criteria. The mean and standard deviations for the FRES, FKG, SMOG, GFI, and DISCERN scores were 49 (13.1), 10.9 (2.5), 10.2 (1.9), 13.6 (2.4), and 51.1 (12.8), respectively. A positive correlation was noted between the FRES and DISCERN (R=0.357, p=0.041). Online information on FESS is generally written above the recommended reading levels and of fair quality.