18,815 research outputs found
Second-Level Digital Divide: Mapping Differences in People's Online Skills
Much of the existing approach to the digital divide suffers from an important
limitation. It is based on a binary classification of Internet use by only
considering whether someone is or is not an Internet user. To remedy this
shortcoming, this project looks at the differences in people's level of skill
with respect to finding information online. Findings suggest that people search
for content in a myriad of ways and there is a large variance in how long
people take to find various types of information online. Data are collected to
see how user demographics, users' social support networks, people's experience
with the medium, and their autonomy of use influence their level of user
sophistication.Comment: 29th TPRC Conference, 200
Why We Read Wikipedia
Wikipedia is one of the most popular sites on the Web, with millions of users
relying on it to satisfy a broad range of information needs every day. Although
it is crucial to understand what exactly these needs are in order to be able to
meet them, little is currently known about why users visit Wikipedia. The goal
of this paper is to fill this gap by combining a survey of Wikipedia readers
with a log-based analysis of user activity. Based on an initial series of user
surveys, we build a taxonomy of Wikipedia use cases along several dimensions,
capturing users' motivations to visit Wikipedia, the depth of knowledge they
are seeking, and their knowledge of the topic of interest prior to visiting
Wikipedia. Then, we quantify the prevalence of these use cases via a
large-scale user survey conducted on live Wikipedia with almost 30,000
responses. Our analyses highlight the variety of factors driving users to
Wikipedia, such as current events, media coverage of a topic, personal
curiosity, work or school assignments, or boredom. Finally, we match survey
responses to the respondents' digital traces in Wikipedia's server logs,
enabling the discovery of behavioral patterns associated with specific use
cases. For instance, we observe long and fast-paced page sequences across
topics for users who are bored or exploring randomly, whereas those using
Wikipedia for work or school spend more time on individual articles focused on
topics such as science. Our findings advance our understanding of reader
motivations and behavior on Wikipedia and can have implications for developers
aiming to improve Wikipedia's user experience, editors striving to cater to
their readers' needs, third-party services (such as search engines) providing
access to Wikipedia content, and researchers aiming to build tools such as
recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table
Staging Transformations for Multimodal Web Interaction Management
Multimodal interfaces are becoming increasingly ubiquitous with the advent of
mobile devices, accessibility considerations, and novel software technologies
that combine diverse interaction media. In addition to improving access and
delivery capabilities, such interfaces enable flexible and personalized dialogs
with websites, much like a conversation between humans. In this paper, we
present a software framework for multimodal web interaction management that
supports mixed-initiative dialogs between users and websites. A
mixed-initiative dialog is one where the user and the website take turns
changing the flow of interaction. The framework supports the functional
specification and realization of such dialogs using staging transformations --
a theory for representing and reasoning about dialogs based on partial input.
It supports multiple interaction interfaces, and offers sessioning, caching,
and co-ordination functions through the use of an interaction manager. Two case
studies are presented to illustrate the promise of this approach.Comment: Describes framework and software architecture for multimodal web
interaction managemen
Evaluating the End-User Experience of Private Browsing Mode
Nowadays, all major web browsers have a private browsing mode. However, the
mode's benefits and limitations are not particularly understood. Through the
use of survey studies, prior work has found that most users are either unaware
of private browsing or do not use it. Further, those who do use private
browsing generally have misconceptions about what protection it provides.
However, prior work has not investigated \emph{why} users misunderstand the
benefits and limitations of private browsing. In this work, we do so by
designing and conducting a three-part study: (1) an analytical approach
combining cognitive walkthrough and heuristic evaluation to inspect the user
interface of private mode in different browsers; (2) a qualitative,
interview-based study to explore users' mental models of private browsing and
its security goals; (3) a participatory design study to investigate why
existing browser disclosures, the in-browser explanations of private browsing
mode, do not communicate the security goals of private browsing to users.
Participants critiqued the browser disclosures of three web browsers: Brave,
Firefox, and Google Chrome, and then designed new ones. We find that the user
interface of private mode in different web browsers violates several
well-established design guidelines and heuristics. Further, most participants
had incorrect mental models of private browsing, influencing their
understanding and usage of private mode. Additionally, we find that existing
browser disclosures are not only vague, but also misleading. None of the three
studied browser disclosures communicates or explains the primary security goal
of private browsing. Drawing from the results of our user study, we extract a
set of design recommendations that we encourage browser designers to validate,
in order to design more effective and informative browser disclosures related
to private mode
I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis
Revelations of large scale electronic surveillance and data mining by
governments and corporations have fueled increased adoption of HTTPS. We
present a traffic analysis attack against over 6000 webpages spanning the HTTPS
deployments of 10 widely used, industry-leading websites in areas such as
healthcare, finance, legal services and streaming video. Our attack identifies
individual pages in the same website with 89% accuracy, exposing personal
details including medical conditions, financial and legal affairs and sexual
orientation. We examine evaluation methodology and reveal accuracy variations
as large as 18% caused by assumptions affecting caching and cookies. We present
a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and
demonstrate significantly increased effectiveness of prior defenses in our
evaluation context, inclusive of enabled caching, user-specific cookies and
pages within the same website
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