7,349 research outputs found
Characterizing web pornography consumption from passive measurements
Web pornography represents a large fraction of the Internet traffic, with
thousands of websites and millions of users. Studying web pornography
consumption allows understanding human behaviors and it is crucial for medical
and psychological research. However, given the lack of public data, these works
typically build on surveys, limited by different factors, e.g. unreliable
answers that volunteers may (involuntarily) provide.
In this work, we collect anonymized accesses to pornography websites using
HTTP-level passive traces. Our dataset includes about broadband
subscribers over a period of 3 years. We use it to provide quantitative
information about the interactions of users with pornographic websites,
focusing on time and frequency of use, habits, and trends. We distribute our
anonymized dataset to the community to ease reproducibility and allow further
studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages,
7 figure
Agents, Bookmarks and Clicks: A topical model of Web traffic
Analysis of aggregate and individual Web traffic has shown that PageRank is a
poor model of how people navigate the Web. Using the empirical traffic patterns
generated by a thousand users, we characterize several properties of Web
traffic that cannot be reproduced by Markovian models. We examine both
aggregate statistics capturing collective behavior, such as page and link
traffic, and individual statistics, such as entropy and session size. No model
currently explains all of these empirical observations simultaneously. We show
that all of these traffic patterns can be explained by an agent-based model
that takes into account several realistic browsing behaviors. First, agents
maintain individual lists of bookmarks (a non-Markovian memory mechanism) that
are used as teleportation targets. Second, agents can retreat along visited
links, a branching mechanism that also allows us to reproduce behaviors such as
the use of a back button and tabbed browsing. Finally, agents are sustained by
visiting novel pages of topical interest, with adjacent pages being more
topically related to each other than distant ones. This modulates the
probability that an agent continues to browse or starts a new session, allowing
us to recreate heterogeneous session lengths. The resulting model is capable of
reproducing the collective and individual behaviors we observe in the empirical
data, reconciling the narrowly focused browsing patterns of individual users
with the extreme heterogeneity of aggregate traffic measurements. This result
allows us to identify a few salient features that are necessary and sufficient
to interpret the browsing patterns observed in our data. In addition to the
descriptive and explanatory power of such a model, our results may lead the way
to more sophisticated, realistic, and effective ranking and crawling
algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in
Proceedings of the 21th ACM conference on Hypertext and Hypermedi
Revealing User Behaviour on the World-Wide Web
This paper presents the results of a qualitative study of user behaviour on the World-Wide Web. Eight participants were filmed whilst performing user-defined tasks and then asked to review the video-taped session during prompted recall. This data forms the basis for a series of descriptions of user behaviour and the postulation of a number of underlying cognitive mechanisms. Our results indicate that people: lack ready made search strategies, prefer alternatives that are visible, immediately available and familiar, choose the path of least resistance, exhibit social forms of behaviour, engage in parallel activities, object to misleadingly presented information, have trouble orienting, are late in using appropriate strategies, are sensitive to matters of time, and are emotionally involved in the activity. The paper ends with a discussion of how these results can contribute to our understanding of hypermedia
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Analysing web search logs to determine session boundaries for user-oriented learning
Incremental learning approaches based on user search activities provide a means of building adaptive information retrieval systems. To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we run a high risk of grouping together activities that are unrelated or perhaps not from the same user. We are interested in detecting boundaries of sequences between related activities (sessions) that would group the activities for a learning purpose. Session boundaries, in Reuters transaction logs, were detected automatically. The generated boundaries were compared with human judgements. The comparison confirmed that a meaningful session threshold for establishing these session boundaries was confined to a 11-15 minute range
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
Moving Usability Testing onto the Web
Abstract: In order to remotely obtain detailed usability data by tracking user behaviors
within a given web site, a server-based usability testing environment has been
created. Web pages are annotated in such a way that arbitrary user actions (such as
"mouse over link" or "click back button") can be selected for logging. In addition,
the system allows the experiment designer to interleave interactive questions into
the usability evaluation, which for instance could be triggered by a particular sequence
of actions. The system works in conjunction with clustering and visualization
algorithms that can be applied to the resulting log file data. A first version of
the system has been used successfully to carry out a web usability evaluation
Determining WWW User's Next Access and Its Application to Pre-fetching
World-Wide Web (WWW) services have grown to levels where significant delays are expected to happen. Techniques like pre-fetching are likely to help users to personalize their needs, reducing their waiting times. However, pre-fetching is only effective if the right documents are identified and if user's move is correctly predicted. Otherwise, pre-fetching will only waste bandwidth. Therefore, it is productive to determine whether a revisit will occur or not, before starting pre-fetching.
In this paper we develop two user models that help determining user's next move. One model uses Random Walk approximation and the other is based on Digital Signal Processing techniques. We also give hints on how to use such models with a simple pre-fetching technique that we are developing.CNP
Characterizations of User Web Revisit Behavior
In this article we update and extend on earlier long-term studies on user's page revisit behavior. Revisits ar
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