The Snippets Taxonomy in Web Search Engines

In this paper authors analyzed 50 000 keywords results collected from localized Polish Google search engine. We proposed a taxonomy for snippets displayed in search results as regular, rich, news, featured and entity types snippets. We observed some correlations between overlapping snippets in the same keywords. Results show that commercial keywords do not cause results having rich or entity types snippets, whereas keywords resulting with snippets are not commercial nature. We found that significant number of snippets are scholarly articles and rich cards carousel. We conclude our findings with conclusion and research limitations.Comment: 12 pages, 3 table

Navigating the Web: A Qualitative Eye Tracking-based Study of Translators’ Web Search Behaviour

This Element reports an investigation of translators’ use of web-based resources and search engines. The study adopted a qualitative eye tracking-based methodology utilising a combination of gaze replay and retrospective think aloud (RTA) to elicit data. The main contribution of this Element lies in presenting not only an alternative eye tracking methodology for investigating translators’ web search behaviour but also a systematic approach to gauging the reasoning behind translators’ highly complex and context-dependent interaction with search engines and the Web

Featured Snippets Results in Google Web Search: An Exploratory Study

In this paper authors analyzed 163412 keywords and results with featured snippets collected from localized Polish Google search engine. A method-ology for retrieving data from Google search engine was proposed in terms of obtaining necessary data to study featured snippets. It was observed that almost half of featured snippets (48%) is taken from result on first ranking position. Furthermore, some correlations between prepositions and the most often appearing content words in keywords was discovered. Results show that featured snippets are often taken from trustworthy websites like e.g., Wikipedia and are mainly presented in form of a paragraph. Paragraph can be read by Google Assistant or Home Assistant with voice search. We conclude our findings with discussion and research limitations.Comment: 10 pages, 6 tables, accepted to conference ICMarktech'1

Navigating the Web. A Qualitative Eye Tracking–Based Study of Translators' Web Search Behaviour

This Element reports an investigation of translators' use of web-based resources and search engines. The study adopted a qualitative eye tracking-based methodology utilising a combination of gaze replay and retrospective think aloud (RTA) to elicit data. The main contribution of this Element lies in presenting not only an alternative eye tracking methodology for investigating translators' web search behaviour but also a systematic approach to gauging the reasoning behind translators' highly complex and context-dependent interaction with search engines and the Web

Evaluating the retrieval effectiveness of Web search engines using a representative query sample

Search engine retrieval effectiveness studies are usually small-scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, data was collected using specialised software, the Relevance Assessment Tool (RAT). We found that while Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3 per cent of cases whereas Bing only found the correct answer 76.6 per cent of the time. We conclude that search engine performance on navigational queries is of great importance, as users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines

The Evolution of Web Search User Interfaces -- An Archaeological Analysis of Google Search Engine Result Pages

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.Comment: 10 pages, Full Paper of CHIIR 202

Shortcuts to trust: relying on cues to judge online news from unfamiliar sources on digital platforms

Scholarship has increasingly sought solutions for reversing broad declines in levels of trust in news in many countries. Some have advocated for news organizations to adopt strategies around transparency or audience engagement, but there is limited evidence about whether such strategies are effective, especially in the context of news consumption on digital platforms where audiences may be particularly likely to encounter news from sources previously unknown to them. In this paper, we use a bottom-up approach to understand how people evaluate the trustworthiness of online news. We inductively analyze interviews and focus groups with 232 people in four countries (Brazil, India, the United Kingdom, and the United States) to understand how they judge the trustworthiness of news when unfamiliar with the source. Drawing on prior credibility research, we identify three general categories of cues that are central to heuristic evaluations of news trustworthiness online when brands are unfamiliar: content, social, and platform cues. These cues varied minimally across countries, although larger differences were observed by platform. We discuss implications of these findings for scholarship and trust-building efforts

Technologies for extracting and analysing the credibility of health-related online content

The evolution of the Web has led to an improvement in information accessibility. This change has allowed access to more varied content at greater speed, but we must also be aware of the dangers involved. The results offered may be unreliable, inadequate, or of poor quality, leading to misinformation. This can have a greater or lesser impact depending on the domain, but is particularly sensitive when it comes to health-related content. In this thesis, we focus in the development of methods to automatically assess credibility. We also studied the reliability of the new Large Language Models (LLMs) to answer health questions. Finally, we also present a set of tools that might help in the massive analysis of web textual content