1,869 research outputs found
Social Dynamics of Digg
Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.Comment: arXiv admin note: text overlap with arXiv:1010.023
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Why does attention to web articles fall with time?
We analyze access statistics of a hundred and fifty blog entries and news
articles, for periods of up to three years. Access rate falls as an inverse
power of time passed since publication. The power law holds for periods of up
to thousand days. The exponents are different for different blogs and are
distributed between 0.6 and 3.2. We argue that the decay of attention to a web
article is caused by the link to it first dropping down the list of links on
the website's front page, and then disappearing from the front page and its
subsequent movement further into background. The other proposed explanations
that use a decaying with time novelty factor, or some intricate theory of human
dynamics cannot explain all of the experimental observations.Comment: To appear in JASIS
Stochastic model checking for predicting component failures and service availability
When a component fails in a critical communications service, how urgent is a repair? If we repair within 1 hour, 2 hours, or
n hours, how does this affect the likelihood of service failure? Can a formal model support assessing the impact, prioritisation, and
scheduling of repairs in the event of component failures, and forecasting of maintenance costs? These are some of the questions
posed to us by a large organisation and here we report on our experience of developing a stochastic framework based on a discrete
space model and temporal logic to answer them. We define and explore both standard steady-state and transient temporal logic
properties concerning the likelihood of service failure within certain time bounds, forecasting maintenance costs, and we introduce a
new concept of envelopes of behaviour that quantify the effect of the status of lower level components on service availability. The
resulting model is highly parameterised and user interaction for experimentation is supported by a lightweight, web-based interface
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
One goal of online social recommendation systems is to harness the wisdom of
crowds in order to identify high quality content. Yet the sequential voting
mechanisms that are commonly used by these systems are at odds with existing
theoretical and empirical literature on optimal aggregation. This literature
suggests that sequential voting will promote herding---the tendency for
individuals to copy the decisions of others around them---and hence lead to
suboptimal content recommendation. Is there a problem with our practice, or a
problem with our theory? Previous attempts at answering this question have been
limited by a lack of objective measurements of content quality. Quality is
typically defined endogenously as the popularity of content in absence of
social influence. The flaw of this metric is its presupposition that the
preferences of the crowd are aligned with underlying quality. Domains in which
content quality can be defined exogenously and measured objectively are thus
needed in order to better assess the design choices of social recommendation
systems. In this work, we look to the domain of education, where content
quality can be measured via how well students are able to learn from the
material presented to them. Through a behavioral experiment involving a
simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we
show that sequential voting systems can surface better content than systems
that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and
Social Media (ICWSM) 201
- …