A Visual Exploratory Search Engine Solution Based on Cloud Computing

Googling for Health Information

Web search engines have long served as indispensable tools for information retrieval; user behavior and query formulation strategies have been well studied. The introduction of search engines powered by large language models (LLMs) suggested more conversational search and new types of query strategies. In this paper, we compare traditional and LLM-based search for the task of image geolocation, i.e., determining the location where an image was captured. Our work examines user interactions, with a particular focus on query formulation strategies. In our study, 60 participants were assigned either traditional or LLM-based search engines as assistants for geolocation. Participants using traditional search more accurately predicted the location of the image compared to those using the LLM-based search. Distinct strategies emerged between users depending on the type of assistant. Participants using the LLM-based search issued longer, more natural language queries, but had shorter search sessions. When reformulating their search queries, traditional search participants tended to add more terms to their initial queries, whereas participants using the LLM-based search consistently rephrased their initial queries.

Search engines are web-based systems used for retrieving information from the Internet. They have economic power because of their positioning between information providers and information seekers. Search engines can influence the flow of information – possible business transactions – by the way information is indexed, stored, and portrayed as search results. If a search engine provides good coverage of website content from one group of information providers (grouped by country or language) to the detriment of another group, it will have economic implications for both groups. It is known that certain developed countries and the language(s) of these countries, have better coverage than other developed countries and their languages. This study investigates for the first time the website coverage of a developing country, South Africa, and one of its indigenous languages, Afrikaans. What does the existence of search engine country bias and/or linguistic bias imply for developing countries such as South Africa? South African information providers would have reason for concern if information seekers’ attention were continually routed abroad by biased search engines. South African information seekers would also be done an injustice if they cannot find local web content (or content in their indigenous languages) due to poor search engine coverage. Biased search results guide them away from cheaper, more convenient local information due to poor coverage of local content. Search engines are negatively impacted in turn, when users become tired of the poor local coverage and unwanted international search results and turn to other tools, such as local search engines, for information retrieval. How severe are the effects of search engine bias on developing countries such as South Africa? The body of knowledge discussing search engine bias is very limited, and this study is motivated by stating that, given the possibility of negative economic implications of such bias for developing economies, more research on this topic is justified and urgently needed. The study revealed that Western website content enjoys better coverage than South African website content. After further investigation it was also found that English website content enjoys better coverage than website content in Afrikaans. There is, therefore, a proven search engine bias in favour of Western developed countries and the English language. Website visibility is also studied as a possible cause of search engine bias. It would seem plausible that a relationship may exist between the coverage of websites by search engines and how visible these websites are to a search engine’s crawlers. For the determination of website visibility, the number of in links towards each sample domain was determined. This study shows that the higher visibility of websites from developed countries is a cause of search engine bias in favour of these websites. With website visibility proven as a cause of bias, the study indicates that South Africa, and possibly other developing countries, is lagging far behind in the race to create highly visible websites surrounded by well-covered hyperlink structures – the kind of websites most likely to be covered by search engine crawlers. It is the responsibilty of information providers from developing countries to create more hyperlinks between websites from their countries, as well as creating visible website content in their indigenous languages. Search engine coverage bias has negative economic implications for developing countries such as South Africa. This paper investigates the severity of country related coverage bias against websites from the South African domain(s) and the correlation between website coverage and website visibility. Other possible causes of coverage bias found in literature include indexing algorithms, ranking algorithms and lexicons struggling with non-English content. Information providers’ lack of knowledge about website coverage and search engine tools is discussed as another possible cause of country bias.

Searching the Web for information via search engines is a ubiquitous phenomenon and a well‐established field of study in Information Science. Social media sites also continue to evolve and by now have gained enough popularity and momentum to be used as not just vessels for communication with others, but also as important repositories of information. However, it is not clear if the information behavior of users of traditional search engines differ from those performing information searches strictly on social media sites. To address this, we examined data from two user studies on people's exploratory searching behavior: one group only used Web search engines, while the other exclusively used social media sites to search for information. Information search behaviors of both groups regarding exploratory tasks were observed and analyzed through search log and surveys. The results indicate that, while people using social media sites for exploratory search tasks find a smaller quantity and a less diverse set of documents than what they might discover when utilizing traditional Web search engines, they do perceive to end up with more relevant documents. They also report doing less work and feeling less challenged.

This study develops several Google search‐based measures to test the relation between earnings‐week online search results and the speed of price discovery. These measures are based on searches using only a firm's ticker symbol in the search string. I collect the total number of search results (across all search result pages) as well as the type and content of search results on the first three pages of search results. I find that the quantity, quality, and content of search results have varying effects on the speed at which earnings news is impounded into stock price. I also find that effects are only observed for search results presented on the first page of a Google search. Overall, my results suggest that (1) increases in online information are associated with slower price discovery, and (2) the likely mechanism by which this association operates is through useful search results being crowded off the first page of results by more complex or irrelevant search results.

BackgroundOnline information on electronic cigarettes (e-cigarettes) may influence people’s perception and use of e-cigarettes. Websites with information on e-cigarettes in the Chinese language have not been systematically assessed.ObjectiveThe aim of this study was to assess and compare the types and credibility of Web-based information on e-cigarettes identified from Google (in English) and Baidu (in Chinese) search engines.MethodsWe used the keywords vaping or e-cigarettes to conduct a search on Google and the equivalent Chinese characters for Baidu. The first 50 unique and relevant websites from each of the two search engines were included in this analysis. The main characteristics of the websites, credibility of the websites, and claims made on the included websites were systematically assessed and compared.ResultsCompared with websites on Google, more websites on Baidu were owned by manufacturers or retailers (15/50, 30% vs 33/50, 66%; P<.001). None of the Baidu websites, compared to 24% (12/50) of Google websites, were provided by public or health professional institutions. The Baidu websites were more likely to contain e-cigarette advertising (P<.001) and less likely to provide information on health education (P<.001). The overall credibility of the included Baidu websites was lower than that of the Google websites (P<.001). An age restriction warning was shown on all advertising websites from Google (15/15) but only on 10 of the 33 (30%) advertising websites from Baidu (P<.001). Conflicting or unclear health and social claims were common on the included websites.ConclusionsAlthough conflicting or unclear claims on e-cigarettes were common on websites from both Baidu and Google search engines, there was a lack of online information from public health authorities in China. Unbiased information and evidence-based recommendations on e-cigarettes should be provided by public health authorities to help the public make informed decisions regarding the use of e-cigarettes.

Search engines are turning out to be the greatest tools for gaining valuable data from the internet. Search engines return the search result to the user query which can be an important result or non-important result. Because, the users naturally look only at the first few pages of search results, and search engine ranking can introduce significant bias to their understanding of the internet and their information gain. When a search query is delivered to several search engines, each individual returns a list of pages based on the ranking. Scientists have confirmed that merging search results in a meta-search engine makes a substantial progress in a search result. Current meta-search engines use several search engines for fetching the results but do not emphasize on the semantic relation of the query for finding the best result. In order tod overcome this limitation, a new approach is proposed. The proposed approach can optimize meta-search results using the combination of linear search and semantic search.

What's the Story

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines

Exploratory search, in which a user solves complex information problems, is cumbersome with today's search engines. We propose collecting hidden concepts and latent relations (i.e. effective information) from Knowledge-based Question Answering System and system log to study ontology content extension methods, so as to better support exploratory search. We present a model to discover whether two concepts have relevance or not. Methods based on the statistic analysis are introduced to give explanations about the latent relations between concepts. We conduct experiments to evaluate the contribution of the effective information through simulating the exploratory search process. Direct at two search tasks, two different evaluation approaches are introduced.

PurposeThe objective of this paper is to characterize the changes in the rankings of the top ten results of major search engines over time and to compare the rankings between these engines.Design/methodology/approachThe papers compare rankings of the top‐ten results of the search engines Google and AlltheWeb on ten identical queries over a period of three weeks. Only the top‐ten results were considered, since users do not normally inspect more than the first results page returned by a search engine. The experiment was repeated twice, in October 2003 and in January 2004, in order to assess changes to the top‐ten results of some of the queries during the three months interval. In order to assess the changes in the rankings, three measures were computed for each data collection point and each search engine.FindingsThe findings in this paper show that the rankings of AlltheWeb were highly stable over each period, while the rankings of Google underwent constant yet minor changes, with occasional major ones. Changes over time can be explained by the dynamic nature of the web or by fluctuations in the search engines' indexes. The top‐ten results of the two search engines had surprisingly low overlap. With such small overlap, the task of comparing the rankings of the two engines becomes extremely challenging.Originality/valueThe paper shows that because of the abundance of information on the web, ranking search results is of extreme importance. The paper compares several measures for computing the similarity between rankings of search tools, and shows that none of the measures is fully satisfactory as a standalone measure. It also demonstrates the apparent differences in the ranking algorithms of two widely used search engines.

Are Google’s Search Results Unfair or Deceptive Under Section 5 of the FTC Act?

In most major search engines, the interface for image search is the same as traditional Web search: a keyword query followed by a paginated, ranked list of results. Although many image search innovations have appeared in both the literature and on the Web, few have seen widespread use in practice. In this work, we explore the differences between image and general Web search to better support users’ needs. First, we describe some unique characteristics of image search derived through informal interviews with researchers, designers, and managers responsible for building and deploying a major Web search engine. Then, we present results from a large scale analysis of image and Web search logs showing the differences in user behaviour. Grounded in these observations, we present design recommendations for an image search engine supportive of the unique experience of image search. We iterate on a number of designs, and describe a functional prototype that we built.

There are billions of web pages had been hosted in the Internet and the number of documents is increased overtime. But since internet is a huge resource, the problem of how to determine what users need to give good search results is a challenge for many search engines. This paper suggest a framework for building general purposed search engines which can recommend search results (web pages) to user by using a hybrid recommender engine from content-based and collaborative filtering. This framework determines the roles of topics, websites and web pages and give recommendations rely on the mutual relation of the triple topics, websites, and web pages. With this approach, we can improve search quality of search engines and solve some problems that restricted recommender systems. Experimental results show that our search results better than search results of a search engine apply classical Google page rank algorithm (13), a well-known page rank algorithm used in many search engines.

A Study Note On Search Engine Optimization Techniques