31,077 research outputs found
Characterizing and Predicting Email Deferral Behavior
Email triage involves going through unhandled emails and deciding what to do
with them. This familiar process can become increasingly challenging as the
number of unhandled email grows. During a triage session, users commonly defer
handling emails that they cannot immediately deal with to later. These deferred
emails, are often related to tasks that are postponed until the user has more
time or the right information to deal with them. In this paper, through
qualitative interviews and a large-scale log analysis, we study when and what
enterprise email users tend to defer. We found that users are more likely to
defer emails when handling them involves replying, reading carefully, or
clicking on links and attachments. We also learned that the decision to defer
emails depends on many factors such as user's workload and the importance of
the sender. Our qualitative results suggested that deferring is very common,
and our quantitative log analysis confirms that 12% of triage sessions and 16%
of daily active users had at least one deferred email on weekdays. We also
discuss several deferral strategies such as marking emails as unread and
flagging that are reported by our interviewees, and illustrate how such
patterns can be also observed in user logs. Inspired by the characteristics of
deferred emails and contextual factors involved in deciding if an email should
be deferred, we train a classifier for predicting whether a recently triaged
email is actually deferred. Our experimental results suggests that deferral can
be classified with modest effectiveness. Overall, our work provides novel
insights about how users handle their emails and how deferral can be modeled
Studying and Modeling the Connection between People's Preferences and Content Sharing
People regularly share items using online social media. However, people's
decisions around sharing---who shares what to whom and why---are not well
understood. We present a user study involving 87 pairs of Facebook users to
understand how people make their sharing decisions. We find that even when
sharing to a specific individual, people's own preference for an item
(individuation) dominates over the recipient's preferences (altruism). People's
open-ended responses about how they share, however, indicate that they do try
to personalize shares based on the recipient. To explain these contrasting
results, we propose a novel process model of sharing that takes into account
people's preferences and the salience of an item. We also present encouraging
results for a sharing prediction model that incorporates both the senders' and
the recipients' preferences. These results suggest improvements to both
algorithms that support sharing in social media and to information diffusion
models.Comment: CSCW 201
Communication dynamics in finite capacity social networks
In communication networks structure and dynamics are tightly coupled. The
structure controls the flow of information and is itself shaped by the
dynamical process of information exchanged between nodes. In order to reconcile
structure and dynamics, a generic model, based on the local interaction between
nodes, is considered for the communication in large social networks. In
agreement with data from a large human organization, we show that the flow is
non-Markovian and controlled by the temporal limitations of individuals. We
confirm the versatility of our model by predicting simultaneously the
degree-dependent node activity, the balance between information input and
output of nodes and the degree distribution. Finally, we quantify the
limitations to network analysis when it is based on data sampled over a finite
period of time.Comment: Physical Review Letter, accepted (5 pages, 4 figures
Molecular Model of Dynamic Social Network Based on E-mail communication
In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain
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