A Study of Snippet Length and Informativeness: Behaviour, Performance and User Experience

The design and presentation of a Search Engine Results Page (SERP) has been subject to much research. With many contemporary aspects of the SERP now under scrutiny, work still remains in investigating more traditional SERP components, such as the result summary. Prior studies have examined a variety of different aspects of result summaries, but in this paper we investigate the influence of result summary length on search behaviour, performance and user experience. To this end, we designed and conducted a within-subjects experiment using the TREC AQUAINT news collection with 53 participants. Using Kullback-Leibler distance as a measure of information gain, we examined result summaries of different lengths and selected four conditions where the change in information gain was the greatest: (i) title only; (ii) title plus one snippet; (iii) title plus two snippets; and (iv) title plus four snippets. Findings show that participants broadly preferred longer result summaries, as they were perceived to be more informative. However, their performance in terms of correctly identifying relevant documents was similar across all four conditions. Furthermore, while the participants felt that longer summaries were more informative, empirical observations suggest otherwise; while participants were more likely to click on relevant items given longer summaries, they also were more likely to click on non-relevant items. This shows that longer is not necessarily better, though participants perceived that to be the case - and second, they reveal a positive relationship between the length and informativeness of summaries and their attractiveness (i.e. clickthrough rates). These findings show that there are tensions between perception and performance when designing result summaries that need to be taken into account

What snippet size is needed in mobile web search?

A snippet (content summary for a web page) is one of the main elements in a search result page. Search engines have been improved to reduce users' effort in web search, e.g., providing flexible snippet sizes by considering the purpose of the search and suggesting predicted answers. In most cases, search engines for mobile devices present two or three lines of snippet for each result link. Some studies suggest that long snippets provide a better search experience on desktop screens, but this may not be true for mobile devices because of the smaller screen. We conducted a user study to investigate what size of snippet is appropriate for mobile devices. Our findings suggest that users with long snippets on mobile devices exhibit longer search times with no better search accuracy for informational tasks. This is caused by the longer reading time, frequent scrolling with bigger viewport movements, and greater time consumption for searching and reading one result. The overall findings suggest that, unlike desktop users, mobile users are best served by snippets of two to three lines

Toward a meta-vaccine future: Promoting vaccine confidence in the metaverse

The metaverse has a promising role to serve as a global platform and tackle one of the most intractable public health challenges; vaccine hesitancy. Active efforts in this field can enhance vaccine acceptance thus leading to better community health protection. By embracing digital health innovations, the metaverse potentially creates an interactive environment for interdisciplinary collaborations that can foster novel approaches in tackling vaccine hesitancy as well as future pandemics. This paper aims to highlight the unique areas where the metaverse can enhance vaccination confidence, educate about vaccine working principles, and offer collaborative healthcare initiatives in this virtual community

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.This research work has been funded by the Spanish Government through the project TEXT-MESS 2.0 (TIN2009-13391-C04) and by the Valencian Government through projects PROMETEO (PROMETEO/2009/199) and ACOMP/2011/001