10,982 research outputs found
Implicit Measures of Lostness and Success in Web Navigation
In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The userâs task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design
The Impact of Link Suggestions on User Navigation and User Perception
The study reported in this paper explores the effects of providing web users with link suggestions that are relevant to their tasks. Results indicate that link suggestions were positively received. Furthermore, users perceived sites with link suggestions as more usable and themselves as less disoriented. The average task execution time was significantly lower than in the control condition and users appeared to navigate in a more structured manner. Unexpectedly, men took more advantage from link suggestions than women
Understanding user experience of mobile video: Framework, measurement, and optimization
Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the userâs interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining usersâ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account userâs needs and desires when using the service, emphasizing the userâs overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study
Personalize Wayfinding Information for Fire Responders based on Virtual Reality Training Data
Modern buildings with increasing complexity can cause serious difficulties for first responders in emergency wayfinding. While real-time data collection and information analytics become easier in indoor wayfinding, a new challenge has arisen: cognitive overload due to information redundancy. Standardized and universal spatial information systems are still widely used in emergency wayfinding, ignoring first respondersâ individual difference in information intake. This paper proposes and tests the theoretical framework of a spatial information systems for first responders, which reflects their individual difference in information preference and helps reduce the cognitive load in line of duty. The proposed method includes the use of Virtual Reality (VR) experiments to simulate real world buildings, and the modeling of first respondersâ reactions to different information formats and contents in simulated wayfinding tasks. This work is expected to set a foundation of future spatial information system that correctly and effectively responds to first respondersâ needs
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Quantifying, Modeling and Managing How People Interact with Visualizations on the Web
The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage peopleâs open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on peopleâs interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a userâs interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of peopleâs exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in peopleâs interactions with visualizations. 3. Create tools that manage and analyze an audienceâs interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audienceâs interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiencesâ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web
Age, Technology Usage, and Cognitive Characteristics in Relation to Perceived Disorientation and Reported Website Ease of Use
Comparative studies including older and younger adults are becoming more common in HCI, generally used to compare how these two different age groups will approach a task. However, it is unclear whether user 'age' is the underlying factor that differentiates between these two groups. To address this problem, an examination into the relationship between users' age, previous technology experience, and cognitive characteristics is conducted. Measures of perceived disorientation and reported ease of use are used to understand links that exist between these user characteristics and their effect on browsing experience. This is achieved through a lab-based information retrieval task, where participants visited a selection of websites in order to find answers to a series of questions and then self reported their feelings of perceived disorientation and website ease of use through a Likert-scored questionnaire. The presented research found that age accounts for as little as 1% of user browsing experience when performing information retrieval tasks. Further, it showed that cognitive ability and previous technology experience significantly affected perceived disorientation in these searches. These results argue for the inclusion of metrics regarding cognitive ability and previous technology experience when analyzing user satisfaction and performance in Internet based-studies
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