Search results

Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search.

For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks.

Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries.

Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Web‐snippet clustering has recently attracted a lot of attention as a means to provide users with a succinct overview of relevant results compared with traditional search results. This paper seeks to research the building of a web‐snippet clustering system, based on a mixed clustering method.

This paper proposes a mixed clustering method to organise all returned snippets into a hierarchical tree. The method accomplishes two main tasks: one is to construct the cluster labels and the other is to build a hierarchical tree.

Five measures were used to measure the quality of clustering results. Based on the results of the experiments, it was concluded that the performance of the system is better than current commercial and academic systems.

A high performance system is presented, based on the clustering method. A divisive hierarchical clustering algorithm is also developed to organise all returned snippets into a hierarchical tree.

The purpose of this paper is to analyze the readability and level of word complexity of search engine results pages (SERPs) snippets and associated web pages between Google and Bing.

The authors employed the Readability Test Tool to analyze the readability and word complexity of 3,000 SERPs snippets and 3,000 associated pages in Google and Bing retrieved on 150 search queries issued by middle school children.

A significant difference was found in the readability of SERPs snippets and associated web pages between Google and Bing. A significant difference was also observed in the number of complex words in snippets between the two engines but not in associated web pages. At the engine level, the readability of Google and Bing snippets was significantly higher than associated web pages. The readability of Google SERPs snippets was at a much higher level than those of Bing. The readability of snippets in both engines mismatched with the reading comprehension of children in grades 6–8.

The data corpus may be small. Analysis relied on quantitative measures.

Practitioners and other mediators should mitigate the readability issue in SERPs snippets. Researchers should consider text readability and word complexity simultaneously with other factors to obtain the nuanced understanding of young users’ web information behaviors. Additional theoretical and methodological implications are discussed.

This study measured the readability and the level of word complexity embedded in SERPs snippets and compared them to respective web pages in Google and Bing. Findings provide further evidence of the readability issue of SERPs snippets and the need to solve this issue through system design improvements.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

The purpose of this paper is to compare Google Books with WorldCat and to assess some other functionalities of Google Books.

Over 500 random samples generated from WorldCat were searched on Google Books. The search results were used to compare the coverage between Google Books and WorldCat, to estimate the successful link rate to a local library catalogue, the percentage available as full view, snippet, and preview on Google Books, and other services of Google Books.

Google Books can retrieve almost all the books catalogued in WorldCat. Its “Find in a library” link to a local library catalogue works 75 percent of the time. Fewer than 10 percent of Google Books items have free full views, and about 15 percent have snippets and previews, respectively. Previews are much more useful than snippets. Google Books probably indexes books that it does not possess in digital form, in addition to indexing all the books that it has acquired in digital form.

No previous empirical studies of this kind have been found. This study assesses Google Books' coverage and services with quantitative measures.

The purpose of this paper is to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective.

The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results.

The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. The empirical results reveal that PageRank (PR) is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries.

This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar.

The findings indicate that web marketers should pay particular attention to a webpage’s PR, and then place the keyword in URL, the page title, and snippet.

There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort.

It is well known that information behaviour can be biased in countless ways and that users of web search engines have difficulty in assessing the credibility of results. Yet, little is known about how search engine result page (SERP) listings are used to judge credibility and in which if any way such judgements are biased. The paper aims to discuss these issues.

Two studies are presented. The first collects data by means of a controlled, web-based user study (N=105). Studying judgements for three controversial topics, the paper examines the extent to which users agree on credibility, the extent to which judgements relate to those applied by objective assessors and to what extent judgements can be predicted by the users’ position on and prior knowledge of the topic. A second, qualitative study (N=9) utilises the same setup; however, transcribed think-aloud protocols provide an understanding of the cues participants use to estimate credibility.

The first study reveals that users are very uncertain when assessing credibility and their impressions often diverge from objective judges who have fact checked the sources. Little evidence is found indicating that judgements are biased by prior beliefs or knowledge, but differences are observed in the accuracy of judgements across topics. Qualitatively analysing think-aloud transcripts from participants think-aloud reveals ten categories of cues, which participants used to determine the credibility of results. Despite short listings, participants utilised diverse cues for the same listings. Even when the same cues were identified and utilised, different participants often interpreted these differently. Example transcripts show how participants reach varying conclusions, illustrate common mistakes made and highlight problems with existing SERP listings.

This study offers a novel perspective on how the credibility of SERP listings is interpreted when assessing search results. Especially striking is how the same short snippets provide diverse informational cues and how these cues can be interpreted differently depending on the user and his or her background. This finding is significant in terms of how search engine results should be presented and opens up the new challenge of discovering technological solutions, which allow users to better judge the credibility of information sources on the web.

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

The purpose of this paper is to present a topic classification model using the Dewey Decimal Classification (DDC) as the target scheme. This is to be done by exploring metadata as provided by the Open Archives Initiative (OAI) to derive document snippets as minimal document representations. The reason is to reduce the effort of document processing in digital libraries. Further, the paper seeks to perform feature selection and extension by means of social ontologies and related web‐based lexical resources. This is done to provide reliable topic‐related classifications while circumventing the problem of data sparseness. Finally, the paper aims to evaluate the model by means of two language‐specific corpora. The paper bridges digital libraries, on the one hand, and computational linguistics, on the other. The aim is to make accessible computational linguistic methods to provide thematic classifications in digital libraries based on closed topic models such as the DDC.

The approach takes the form of text classification, text‐technology, computational linguistics, computational semantics, and social semantics.

It is shown that SVM‐based classifiers perform best by exploring certain selections of OAI document metadata.

The findings show that it is necessary to further develop SVM‐based DDC‐classifiers by using larger training sets possibly for more than two languages in order to get better F‐measure values.

Algorithmic and formal‐mathematical information is provided on how to build DDC‐classifiers for digital libraries.

The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either.

The authors propose to develop a query-based summarizer, called Q_Sum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. Q_Sum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one.

Experimental results show that Q_Sum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search.

The proposed query-based summarizer, Q_Sum, is unique based on its searching approach. Q_Sum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.