Search engines serve as a primary gateway to the web, and their interfaces must effectively support users' search tasks. Over time, Search Engine Results Pages (SERPs) have evolved from the traditional ''list of 10 blue links'' into sophisticated and diversified interfaces, incorporating a wide range of features to facilitate the search process. However, analyses of SERP elements often suffer from inconsistent terminology, making cross-study comparisons difficult. To address this issue, this work proposes a comprehensive catalog of SERP elements that standardizes their classification. We examined leading web search engines (Google, Bing, Yandex, Yahoo!, Baidu and DuckDuckGo), assigned consistent names to elements, and organized them into groups. In total, 143 elements were identified and classified as organic results, sponsored results, or features. By publishing this catalog on a dedicated website and in a research repository, this work provides a valuable resource for future research on search interfaces.

More and more people are relying on the Web to find health information. Challenges faced by individuals with low health literacy in the real world likely persist in the virtual realm. To assist these users, our first step is to identify them. This study aims to uncover disparities in the information-seeking behavior of users with varying levels of health literacy. We utilized data gathered from a prior user experiment. Our approach involves a classification scheme encompassing events during web search sessions, spanning the browser, search engine, and web pages. Employing this scheme, we logged interactions from video recordings in the user study and subjected the event logs to descriptive and inferential analyses. Our data analysis unveils distinctive patterns within the low health literacy group. They exhibit a higher frequency of query reformulations with entirely new terms, engage in more left clicks, utilize the browser's backward functionality more frequently, and invest more time in interactions, including increased scrolling on results pages. Conversely, the high health literacy group demonstrates a greater propensity to click on universal results, extract text from URLs more often, and make more clicks with the mouse middle button. These findings offer valuable insights for inferring users' health literacy in a non-intrusive manner. The automatic inference of health literacy can pave the way for personalized services, enhancing accessibility to information and education for individuals with low health literacy, among other benefits.

Web search engines have marked everyone’s life by transforming how one searches and accesses information. Search engines give special attention to the user interface, especially search engine result pages (SERP). The well-known “10 blue links” list has evolved into richer interfaces, often personalized to the search query, the user, and other aspects. More than 20 years later, the literature has not adequately portrayed this development. We present a study on the evolution of SERP interfaces during the last two decades using Google Search as a case study. We used the most searched queries by year to extract a sample of SERP from the Internet Archive. Using this dataset, we analyzed how SERP evolved in content, layout, design (e.g., color scheme, text styling, graphics), navigation, and file size. We have also analyzed the user interface design patterns associated with SERP elements. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers. This systematic analysis portrays evolution trends in search engine user interfaces and, more generally, web design. We expect this work will trigger other, more specific studies that can take advantage of our dataset.

Web Search Engine Results Pages (SERP) are one of the most well-known and used web pages. These pages have started as simple “10 blue links” pages, but the information in SERP currently goes way beyond these links. Several features have been included in these pages to complement organic and sponsored results and attempt to provide answers to the query instead of just pointing to websites that might deliver that information. In this work, we analyze the appearance and evolution of SERP features in the two leading web search engines, Google Search and Microsoft Bing. Using a sample of SERP from the Internet Archive, we analyzed the appearance and evolution of these features. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers.

Health literacy affects how people understand health information and, therefore, should be considered by search engines in health searches. In this work, we analyze how the level of health literacy is related to the eye movements of users searching the web for health information. We performed a user study with 30 participants that were asked to search online in the context of three work task situations defined by the authors. Their eye interactions with the Search Results Page and the Result Pages were logged using an eye-tracker and later analyzed. When searching online for health information, people with adequate health literacy spend more time and have more fixations on Search Result Pages. In this type of page, they also pay more attention to the results' hyperlink and snippet and click in more results too. In Result Pages, adequate health literacy users spend more time analyzing textual content than people with lower health literacy. We found statistical differences in terms of clicks, fixations, and time spent that could be used as a starting point for further research. That we know of, this is the first work to use an eye-tracker to explore how users with different health literacy search online for health-related information. As traditional instruments are too intrusive to be used by search engines, an automatic prediction of health literacy would be very useful for this type of system.

Looking for health information is one of the most popular activities online. However, the specificity of language on this domain is frequently an obstacle to comprehension, especially for the ones with lower levels of health literacy. For this reason, search engines should consider the readability of health content and, if possible, adapt it to the user behind the search. In this work, we explore methods to assess the readability of health content automatically. We propose features capable of measuring the specificity of a medical text and estimate the knowledge necessary to comprehend it. The features are based on information retrieval metrics and the log-likelihood of a text with lay and medico-scientific language models. To evaluate our methods, we built and used a dataset composed of health articles of Simple English Wikipedia and the respective documents in ordinary Wikipedia. We achieved a maximum accuracy of 88% in binary classifications (easy versus hard-to-read). We found out that the machine learning algorithm does not significantly interfere with performance. We also experimented and compared different features combinations. The features using the values of the log-likelihood of a text with lay and medico-scientific language models perform better than all the others.

Because of terminology mismatches, health consumers frequently face difficulties while searching the Web for health information. Difficulties arise in query formulation but also in understanding the retrieved documents. In this work we analyze how documents' readability affects users' comprehension and how both affect the retrieval performance, measured in different ways. In addition, we analyze how performance measures relate with each other. For this purpose we have conducted a laboratory user study with 40 participants. We found that readability is essential for a document to be at least partially relevant and that it becomes even more important if the document has medico-scientific terminology. Moreover, the relevance of a document to a specific user highly depends on its comprehension. In lay queries we found the medical accuracy of users' answers is related to the session's relevance assessments. This shows that users can, at least in part, relate their relevance assessments with the medical accuracy of the documents. On the other hand, this relationship does not exist with medico-scientific queries.

Health consumers usually face difficulties on their online searches, mainly because of the differences between terminologies used by laypeople and health professionals. This work presents a tool, HealthTranslator, available as a Google Chrome extension that intends to reduce this terminological gap while users are searching the Web for health information. HealthTranslator automatically annotates medical concepts in web documents, providing additional information, such as concept definition, related concepts and links to external references. The solution was evaluated regarding its: (a) performance - the document processing is done gradually, typically from the top to the bottom of the document and performance was not an issue raised by the users; (b) concept coverage - the solution was compared to a similar extension performing in English recognizing significantly more concepts. A comparison with a corpus of Portuguese documents manually annotated with medical concepts showed an average F-measure between 27% and 33%, depending on the type of concepts being recognized; (c) users' receptivity to HealthTranslator and its usability - many aspects were surveyed on a user study. In general, the extension has a good acceptance and users find it useful.

A patient»s health literacy has a direct impact on their health, but more than a third of the USA population has "basic" or "below basic" levels of health literacy. An individual»s wellbeing is also affected by the communication with their physician, as the use of technical terminology may hinder the patient»s understanding. A patient»s ability to, later on, recall or retrieve helpful information could reduce these comprehension problems and this can be improved by a good management of personal health information. To help overcome some of these problems, we created HealthTalks, a mobile app that empowers the patients, easing their daily health tasks and self-care ability. It does so by recording the audio of a medical appointment, transcribing its dialogue, giving more information about medical concepts employed, and allowing information associated with medical appointments to be easily managed by the patient. Usability tests were conducted with elderly people, ranging from the icons used to the general user experience. Results were very positive, with users accomplishing most tasks successfully and often with the least amount of clicks. We also evaluated the speech recognition software used, Google Cloud Speech API, reaching an error rate of 12 percent in medical texts.

Searching for health information is one of the most popular activities on the web. In this domain, users often misspell or lack knowledge of the proper medical terms to use in queries. To overcome these difficulties and attempt to retrieve higher‐quality content, we developed a query suggestion system that provides alternative queries combining the Portuguese or English language with lay or medico‐scientific terminology. Here we evaluate this system's impact on the medical accuracy of the knowledge acquired during the search. Evaluation shows that simply providing these suggestions contributes to reduce the quantity of incorrect content. This indicates that even when suggestions are not clicked, they are useful either for subsequent queries' formulation or for interpreting search results. Clicking on suggestions, regardless of type, leads to answers with more correct content. An analysis by type of suggestion and user characteristics showed that the benefits of certain languages and terminologies are more perceptible in users with certain levels of English proficiency and health literacy. This suggests a personalization of this suggestion system toward these characteristics. Overall, the effect of language is more preponderant than the effect of terminology. Clicks on English suggestions are clearly preferable to clicks on Portuguese ones.

English is by far the most used language on the web. In some domains, the existence of less content in the users' native language may not be problematic and even help to cope with the information overload. Yet, in domains such as health, where information quality is critical, a larger quantity of information may mean easier access to higher quality content. Query translation may be a good strategy to access content in other languages, but the presence of medical terms in health queries makes the translation process more difficult, even for users with very good language proficiencies. In this study, we evaluate how translating a health query affects users with different language proficiencies. We chose English as the non‐native language because it is a widely spoken language and it is the most used language on the web. Our findings suggest that non‐English–speaking users having at least elementary English proficiency can benefit from a system that suggests English alternatives for their queries, or automatically retrieves English content from a non‐English query. This awareness of the user profile results in higher precision, more accurate medical knowledge, and better access to high‐quality content. Moreover, the suggestions of English‐translated queries may also trigger new health search strategies.

We have conducted a user study to evaluate several generalist and health-specific search engines on health information retrieval. Users evaluated the relevance of the top 30 documents of 4 search engines in two different health information needs. We introduce the concepts of local and global precision and analyze how they affect the evaluation. Results show that Google surpasses the precision of all other engines, including the health-specific ones, and that precision differs with the type of clinical question and its medical specialty.