Clustering-Based Visual Interfaces for Presentation of Web Search Results: An Empirical Investigation Ozgur

2021 ◽  
Author(s):  
Ozgur Turetken ◽  
Ramesh Sharda
Keyword(s):  
Web Search ◽  
Empirical Investigation ◽  
Two Dimensional ◽  
Search Results ◽  
Search Tasks ◽  
Visual Interfaces

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

scholarly journals Clustering-Based Visual Interfaces for Presentation of Web Search Results: An Empirical Investigation Ozgur

2021 ◽  
Author(s):  
Ozgur Turetken ◽  
Ramesh Sharda
Keyword(s):  
Web Search ◽  
Empirical Investigation ◽  
Two Dimensional ◽  
Search Results ◽  
Search Tasks ◽  
Visual Interfaces

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

scholarly journals Can search result summaries enhance the web search efficiency and experiences of the visually impaired users?

2020 ◽  
Author(s):  
Aboubakr Aqle ◽  
Dena Al-Thani ◽  
Ali Jaoua
Keyword(s):  
Search Engine ◽  
Visually Impaired ◽  
Web Search ◽  
Task Completion ◽  
Formal Concept ◽  
Search Efficiency ◽  
User Interactions ◽  
Search Results ◽  
Search Tasks ◽  
Google Search

AbstractThere are limited studies that are addressing the challenges of visually impaired (VI) users when viewing search results on a search engine interface by using a screen reader. This study investigates the effect of providing an overview of search results to VI users. We present a novel interactive search engine interface called InteractSE to support VI users during the results exploration stage in order to improve their interactive experience and web search efficiency. An overview of the search results is generated using an unsupervised machine learning approach to present the discovered concepts via a formal concept analysis that is domain-independent. These concepts are arranged in a multi-level tree following a hierarchical order and covering all retrieved documents that share maximal features. The InteractSE interface was evaluated by 16 legally blind users and compared with the Google search engine interface for complex search tasks. The evaluation results were obtained based on both quantitative (as task completion time) and qualitative (as participants’ feedback) measures. These results are promising and indicate that InteractSE enhances the search efficiency and consequently advances user experience. Our observations and analysis of the user interactions and feedback yielded design suggestions to support VI users when exploring and interacting with search results.

scholarly journals Personalised Web search and user satisfaction: a user-centred evaluation.

2020 ◽  
Vol 25 (4) ◽  
Author(s):  
Sarah Salehi ◽  
◽  
Jia DU ◽  
Helen Ashman ◽  
◽  
...  
Keyword(s):  
University Students ◽  
User Satisfaction ◽  
Information Needs ◽  
Web Search ◽  
Search Time ◽  
Search Results ◽  
Search Tasks ◽  
Academic Information ◽  
Google Search ◽  
Students Satisfaction

Introduction. Most university students depend significantly, sometimes exclusively, on the Google search engine for their academic information needs. User satisfaction leads to users’ deeper engagement with an information system that is shown to improve learning in an educational setting. This paper evaluates students’ satisfaction with results from personalised Web search against non-personalised Web search. Method. During semi-structured study sessions, twenty-eight participants (university students) were required to complete a series of search tasks using both personalised and non-personalised Web search. Analysis. Evaluation was based on participants’ explicit feedback as well as their implicit behaviour including search time, number of queries and clicked result links per task, finding the answer and relevance of the search results. Results. There was no apparent significant increase in the participants’ overall level of satisfaction with personalised search results compared to non-personalised results. However, it was found that personalised search reduced the time spent to finish a task and reduced the number of clicks required to arrive at the selected outcome. Conclusions. Personalisation of search results does not increase students' satisfaction with their search results. However, it does reduce the time spent by students in locating information they judged to be satisfactory answers to their questions.

scholarly journals Characterizing the Influence of Confirmation Bias on Web Search Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Masaki Suzuki ◽  
Yusuke Yamamoto
Keyword(s):  
Health Literacy ◽  
Web Search ◽  
User Study ◽  
Confirmation Bias ◽  
Search Behavior ◽  
Prior Beliefs ◽  
Search Results ◽  
Search Tasks ◽  
Significant Difference ◽  
The Relationship

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.

scholarly journals The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Primary Elections ◽  
Virtual Agents ◽  
Search Results ◽  
Presidential Primary ◽  
Large Scale Analysis ◽  
Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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