Large Search Engine Research Articles

The Legal Significance of Independent Research based on Article 40 DSA for the Management of Systemic Risks in the Digital Services Act

Abstract As part of its risk management system, aiming at the mitigation of so-called systemic risks on very large online platforms and very large online search engines (VLOPSEs), the DSA introduces data access rights for independent researchers. With this instrument, the legislator hopes to enable the production of evidence concerning possible threats to society and democracy that might be linked to these platforms or search engines. After an overview of the functioning of the risk management system in the DSA that is characterised by a collaborative governance framework and a learning-based approach, the article will explore the role of the researchers inside this framework. It thereby focuses on the legal significance the researchers’ evidence could have for the risk mitigation obligations imposed on the VLOPSEs in the DSA by interpreting the provisions of the DSA.

A stochastic model of the economics of Internet search

PurposeInternet search is a $120bn business that answers lists of search terms or keywords with relevant links to Internet webpages. Only a few companies have sufficient scale to compete and thus economics of the process are paramount. This study aims to develop a detailed industry-specific modeling of the economics of internet search.Design/methodology/approachThe current research develops a stochastic model of the process of Internet indexing, search and retrieval in order to predict expected costs and revenues of particular configurations and usages.FindingsThe models define behavior and economics of parameters that are not directly observable, where it is difficult to empirically determine the distributions and economics.Originality/valueThe model may be used to guide the economics of large search engine operations, including the advertising platforms that depend on them and largely fund them.

How Do Implausible Tensile Price Claims Affect Consumer Perceptions of Deceptiveness and Consumer Willingness to Purchase in Online Environments?

The study explores how and why an embedded implausible tensile price claim (TPC) affects consumers’ decisions as to whether to click on the associated online advertisement and their subsequent purchases. The authors conduct a field experiment by designing a structured set of search advertisements with implausible TPCs for a global apparel brand and launching them on Baidu, the largest search engine in China. Using a hierarchical Bayesian model, the authors quantify how implausible TPCs influence consumers’ two-stage decisions: clicking on the advertisement and purchasing the product. Using two laboratory experiments, the authors further demonstrate that the purchase decision is mediated by consumers’ perceptions of deception in the advertisement, despite the fact that the click decision is not. Results show that implausible TPCs in a search advertisement motivate consumers to click on the promotion. However, the discrepancy between the advertised and the actual discounts makes the advertisement seem deceptive to consumers, which reduces their likelihood of purchasing the product and leads them to search for other products and promotions. The results address advertising deception in online price claims, offer critical policy implications regarding implausible TPCs, and shed light on designing and executing implausible TPCs in online advertising.

Is ChatGPT-like technology going to replace commercial search engines?

PurposeThis paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.Design/methodology/approachIn recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.FindingsThree major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.Originality/valueThis paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

The Digital Services Act’s red line: what the Commission can and cannot do about disinformation

ABSTRACT The Digital Services Act (DSA) creates a system of general risk management that is composed of two main obligations: risk assessment (Article 34), and risk mitigation (Article 35). The obligations are mandatory for very large online platforms and search engines (VLOPs/VLOSEs). The adoption of the risk-based approach to digital services make the law more future-proof. But inevitably it also makes the law very vague. This vagueness of the statutory language causes some to suggest that the European Commission will inevitably become the proverbial Ministry of Truth when tackling disinformation. This article argues that upon closer reading of the DSA, and its constitutional context, the worries that the Commission inevitably becomes a Ministry of Truth are misplaced. Suppressing incorrect or misleading lawful information is not the goal of the DSA. That is not to say that the DSA cannot be abused. But the law is not pre-programmed to do so.

The urban community college: An overview

AbstractUrban community colleges have been studied primarily from a case study perspective, often highlighted by doctoral students conducting research at their institutions. Little of this scholarship, however, has been translated into the base of literature, as less than 5% of the citations in the largest search engines are focused on urban community colleges. The article begins by exploring what “urban” means, primarily using the complex and evolving definition from the US Census Bureau. Then it explores this case study research on urban community colleges' mission, organization, students, and faculty. The article concludes with a strong recommendation for identifying and making available rigorous data to guide a more systematic approach to studying urban community colleges.

Assessing the Quality and Readability of Web-Based Information on Otoplasty.

Introduction: As health care-related internet use expands, parents and patients considering otoplasty frequently turn to online resources for further information to aid their decision making. Objective: In online resources related to otoplasty, what is the quality of the information and how readable is it based on standardized tests? Methods: A web search was performed on the three major search engines, using search terms related to otoplasty. The top 20 results from each search engine were selected. The DISCERN instrument was applied to assess the quality of health information, whereas the Flesch-Kincaid readability tests were used to assess readability. Results: The mean DISCERN score was 28.7 out of a possible score of 80 (poor quality) and commercial bias was common. The Flesch-Kincaid readability test results were significantly higher than the average reading level for adults in the United Kingdom. On the largest search engine, there was a correlation between search engine ranking and both readability and quality. Conclusion: Online patient information resources on otoplasty are generally of poor quality and difficult to read for the average patient.

Identifying the Risk Regions of Wild Boar (Sus scrofa) Incidents in China.

The objectives of this study were to identify the risk regions of wild boar incidents in China and to draw a risk map. Risk maps can be used to plan the prioritization of preventive measures, increasing management effectiveness from both a short- and a long-term perspective. We used a web crawler (web information access technology) to obtain reports of wild boar incidents from China's largest search engine (Baidu) and obtained 196 valid geographic locations of wild boar incidents from the reports. Subsequently, a system of environmental variables-with climate, topography, landscape, and human disturbance as the main variable types-was constructed, based on human-land-system thinking. Finally, the Maxent model was applied to predict the risk space of wild boar incidents in China by integrating the geographic location information for wild boar incidents with the environmental variables. We observed that the types of environmental variables that contributed to wild boar incidents were in the descending order of climate (40.5%) > human disturbance (25.2%) > landscape (24.4%) > topography (9.8%). Among the 14 environmental variables, annual precipitation, the GDP index, and the mean annual temperature were the main environmental variables. The distance from woodland, distance from cultivated land, and elevation were the secondary environmental variables. The response curves of the environmental variables demonstrated that the highest probability of wild boar incidents occurred when the annual average temperature was 16 °C, the annual precipitation was 800 mm, and the altitudes were 150 m and 1800 m. The probability of wild boar incidents decreased with an increase in the distance from cultivated and forested land, and increased sharply and then levelled off with an increase in the GDP index. Approximately 12.18% of China was identified as being at a high risk of wild boar incidents, mainly on the eastern side of the Huhuanyong Line.

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

AbstractThis paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

How Does Google Adapt to Expand its Business Footprint into China

In this article, the method of case analysis is adopted. Google is the world's largest search engine. But in recent years, after GDPR came down, Google has unwittingly lost tens of billions of dollars while weighing the pros and cons. Then it studies the dilemma of Google in entering the Chinese market. The biggest problems are found in the country's social system, culture shock and differences in the market. In their exploration, they found that if they could localize their products, it would be a big step towards completely capturing the huge market in China. At the same time, another key point is at the top of the company.

The Digital Services Act (DSA): A New Era for Online Harms and Intermediary Liability

The EU’s Digital Services Act (DSA) marks the biggest shake up to the rules for online intermediary liability in twenty years. The DSA is accompanied by flanking instruments regulating terrorist content, child sexual abuse material (CSAM) and political ads which, together, will create an entirely new framework for the regulation of online harms in the EU. These new and wide-ranging obligations attempt to reconcile the damage caused by unregulated user-generated content, fundamental rights to freedom of information and the practical limitations of moderating content at scale. The DSA is likely to shape the global approach to content regulation in this emerging area of law. Digital Services Act, DSA, Online Harm, Intermediary Liability, Intermediary Services, Hosting Services, Very Large Online Search Engines, Online Platforms, Very Large Online Platforms

Urbanites’ mental health undermined by air pollution

The rising mental health difficulties of the urban population in developing countries may be attributed to the high levels of air pollution. However, nationwide large-scale empirical works that examine this claim are rare. In this study, we construct a daily mental health metric using the volume of mental-health-related queries on the largest search engine in China, Baidu, to test this hypothesis. We find that air pollution causally undermines people’s mental health and that this impact becomes stronger as the duration of exposure to air pollution increases. Heterogeneity analyses reveal that men, middle-aged people and married people are more vulnerable to the impact of air pollution on mental health. More importantly, the results also demonstrate that the cumulative effects of air pollution on mental health are smaller for people living in cities with a higher gross domestic product per capita, more health resources, larger areas of green land and more sports facilities. Finally, we estimate that with a one-standard-deviation increase of fine particulate matter (26.3 μg m−3), the number of people who suffer from mental health problems in China increases by approximately 1.15 million. Our findings provide quantitative evidence for the benefits of reducing air pollution to promote mental health and well-being.

Search Engine Optimization : An overview

In today’s world internet is growing with each passing day. Data is very huge and complex. To sort and manage this huge amount of data over large database search engine plays a very important role. Google is the basic and most widely used search engine through which we can optimize the website. With the help of search engine, we can actively gather the visitors to our site. Search engine is the most common part of our daily life. Because of this reason, a search engine plays a very important role as people can search and explore or find according to their needs.

Developing a Meta-Suggestion Engine for Search Queries

Typically, search engines provide query suggestions to assist users in the search process. Query suggestions are very important for improving users search experience. However, most query suggestions are based on the user's search logs, and they can be influenced by infrequently searched queries. Depending on the user's query, query suggestions can be ineffective in global search engines but effective in a domestic search engine. Conversely, it can be effective in global engines and weak in domestic engines. In addition, log-based query suggestions require many search logs, which makes them difficult to construct outside of a large search engine. Some search engines do not provide query suggestions, making searches difficult for users. These query suggestion vulnerabilities degrade the user's search experience. In this study, we develop a meta-suggestion, a new query suggestion scheme. Similar to meta-searches, meta-suggestions retrieves candidate queries of suggestions from other search engines. Meta-suggestions generate suggestions by reranking the aggregated candidate queries. We develop a meta-suggestion engine (MSE) browser extension that generates meta-suggestions. It can provide query suggestions for any webpage and does not require a search log. Comparing our meta-suggestions to major search engines such as Google, showed a 17% performance improvement on normalized discounted cumulative gain (NDCG) and a 31% improvement on precision. If more detailed factors, such as user preferences are discovered through continued research, it is expected that user searches will greatly improve. An enhanced user search experience is possible if factors, such as user preference, are examined in future work.

Neighborhood level chronic respiratory disease prevalence estimation using search query data.

Estimation of disease prevalence at sub-city neighborhood scale allows early and targeted interventions that can help save lives and reduce public health burdens. However, the cost-prohibitive nature of highly localized data collection and sparsity of representative signals, has made it challenging to identify neighborhood scale prevalence of disease. To overcome this challenge, we utilize alternative data sources, which are both less sparse and representative of localized disease prevalence: using query data from a large commercial search engine, we identify the prevalence of respiratory illness in the United States, localized to census tract geographic granularity. Focusing on asthma and Chronic Obstructive Pulmonary Disease (COPD), we construct a set of features based on searches for symptoms, medications, and disease-related information, and use these to identify illness rates in more than 23 thousand tracts in 500 cities across the United States. Out of sample model estimates from search data alone correlate with ground truth illness rate estimates from the CDC at 0.69 to 0.76, with simple additions to these models raising those correlations to as high as 0.84. We then show that in practice search query data can be added to other relevant data such as census or land cover data to boost results, with models that incorporate all data sources correlating with ground truth data at 0.91 for asthma and 0.88 for COPD.

An Assessment of Available Information on the World Wide Web for Patients with Lower Limb Arterial Disease

The quality of patient information relating to intermittent claudication (IC) and peripheral arterial disease (PAD) on the World Wide Web was assessed. The quality of websites and YouTube videos was assessed using the search terms "intermittent claudication" and "peripheral arterial disease". The first 50 hits screened for each search term from the three largest search engines by market share, and the first 20 videos from YouTube were screened. Website quality was scored using the University of Michigan Consumer Health Website tool (maximum score 80). Readability was calculated using the Flesch Reading Ease (FRE) score (maximum score 100). Videos were classified by content and upload source. Video reliability was assessed using the JAMA benchmark criteria. Video educational content was assessed using the Global Quality Score (GQS). Subjective content assessment was undertaken. Seventy-six websites were analysed. The majority of websites for both IC (51.7%) and PAD (72.3%) were rated as weak. The median Michigan score for IC (27; interquartile range [IQR] 15, 32.5) was lower that the score for PAD (31; IQR 25.5, 38.8; p= .030). The majority of websites for both IC (69%) and PAD (68.1%) were rated as requiring an above average reading level. The overall median FRE score was 55.9 (IQR 46.6, 60.6) for IC and 55.3 (IQR 44.6, 59.3) for PAD. Twenty-two videos were analysed. Of the 14 videos evaluated as part of the PAD search, the median JAMA score was 2 (2 - 3), the median GQS score was 3 (3 - 3) and the evaluation of content score was 8.5 (7.25 - 11.5). The equivalent scores for the IC search were 2 (2 - 2), 3 (3 - 4), and 5.5 (5 - 8). The educational quality and reliability of information both in written and video form on the internet is low. Attention needs to focus on improving the quality of all forms of information delivery to allow proper advocacy for patients.

Experimental Framework to Simulate Rescue Operations after a Natural Disaster

Computational simulation is a powerful tool for performance evaluation of computational systems. It is useful to make capacity planning of data center clusters, to obtain profiling reports of software applications and to detect bottlenecks. It has been used in different research areas like large scale Web search engines, natural disaster evacuations, computational biology, human behavior and tendency, among many others. However, properly tuning the parameters of the simulators, defining the scenarios to be simulated and collecting the data traces is not an easy task. It is an incremental process which requires constantly comparing the estimated metrics and the flow of simulated actions against real data. In this work, we present an experimental framework designed for the development of large scale simulations of two applications used upon the occurrence of a natural disaster strikes. The first one is a social application aimed to register volunteers and manage emergency campaigns and tasks. The second one is a benchmark application a data repository named MongoDB. The applications are deployed in a distributed platform which combines different technologies like a Proxy, a Containers Orchestrator, Containers and a NoSQL Database. We simulate both applications and the architecture platform. We validate our simulators using real traces collected during simulacrums of emergency situations.

Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018.

BackgroundBorreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany.ObjectiveThe aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies.MethodsGoogle AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature.ResultsA total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (r=0.88), and temperature and tick bite (r=0.83), and a very high correlation between borreliosis and tick bite (r=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg r=0.80, Mecklenburg-West Pomerania r= 0.77, Saxony r= 0.74, and Saxony-Anhalt r=0.90; all P<.001).ConclusionsOur study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases.

Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube

Search engines are the primary gateways of information. Yet, they do not take into account the credibility of search results. There is a growing concern that YouTube, the second largest search engine and the most popular video-sharing platform, has been promoting and recommending misinformative content for certain search topics. In this study, we audit YouTube to verify those claims. Our audit experiments investigate whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying misinformation. After shortlisting five popular topics known to contain misinformative content and compiling associated search queries representing them, we conduct two sets of audits-Search-and Watch-misinformative audits. Our audits resulted in a dataset of more than 56K videos compiled to link stance (whether promoting misinformation or not) with the personalization attribute audited. Our videos correspond to three major YouTube components: search results, Up-Next, and Top 5 recommendations. We find that demographics, such as, gender, age, and geolocation do not have a significant effect on amplifying misinformation in returned search results for users with brand new accounts. On the other hand, once a user develops a watch history, these attributes do affect the extent of misinformation recommended to them. Further analyses reveal a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations. In conclusion, YouTube still has a long way to go to mitigate misinformation on its platform.

Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

Reporting on brucellosis, a relatively rare infectious disease caused by Brucella, is often delayed or incomplete in traditional disease surveillance systems in China. Internet search engine data related to brucellosis can provide an economical and efficient complement to a conventional surveillance system because people tend to seek brucellosis-related health information from Baidu, the largest search engine in China. In this study, brucellosis incidence data reported by the CDC of China and Baidu index data were gathered to evaluate the relationship between them. We applied an autoregressive integrated moving average (ARIMA) model and an ARIMA model with Baidu search index data as the external variable (ARIMAX) to predict the incidence of brucellosis. The two models based on brucellosis incidence data were then compared, and the ARIMAX model performed better in all the measurements we applied. Our results illustrate that Baidu index data can enhance the traditional surveillance system to monitor and predict brucellosis epidemics in China.