94,706 research outputs found
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Directionality is a crucial ingredient in many complex networks in which
information, energy or influence are transmitted. In such directed networks,
analysing flows (and not only the strength of connections) is crucial to reveal
important features of the network that might go undetected if the orientation
of connections is ignored. We showcase here a flow-based approach for community
detection in networks through the study of the network of the most influential
Twitter users during the 2011 riots in England. Firstly, we use directed Markov
Stability to extract descriptions of the network at different levels of
coarseness in terms of interest communities, i.e., groups of nodes within which
flows of information are contained and reinforced. Such interest communities
reveal user groupings according to location, profession, employer, and topic.
The study of flows also allows us to generate an interest distance, which
affords a personalised view of the attention in the network as viewed from the
vantage point of any given user. Secondly, we analyse the profiles of incoming
and outgoing long-range flows with a combined approach of role-based similarity
and the novel relaxed minimum spanning tree algorithm to reveal that the users
in the network can be classified into five roles. These flow roles go beyond
the standard leader/follower dichotomy and differ from classifications based on
regular/structural equivalence. We then show that the interest communities fall
into distinct informational organigrams characterised by a different mix of
user roles reflecting the quality of dialogue within them. Our generic
framework can be used to provide insight into how flows are generated,
distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from:
http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or
http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC
When-To-Post on Social Networks
For many users on social networks, one of the goals when broadcasting content
is to reach a large audience. The probability of receiving reactions to a
message differs for each user and depends on various factors, such as location,
daily and weekly behavior patterns and the visibility of the message. While
previous work has focused on overall network dynamics and message flow
cascades, the problem of recommending personalized posting times has remained
an underexplored topic of research. In this study, we formulate a when-to-post
problem, where the objective is to find the best times for a user to post on
social networks in order to maximize the probability of audience responses. To
understand the complexity of the problem, we examine user behavior in terms of
post-to-reaction times, and compare cross-network and cross-city weekly
reaction behavior for users in different cities, on both Twitter and Facebook.
We perform this analysis on over a billion posted messages and observed
reactions, and propose multiple approaches for generating personalized posting
schedules. We empirically assess these schedules on a sampled user set of 0.5
million active users and more than 25 million messages observed over a 56 day
period. We show that users see a reaction gain of up to 17% on Facebook and 4%
on Twitter when the recommended posting times are used. We open the dataset
used in this study, which includes timestamps for over 144 million posts and
over 1.1 billion reactions. The personalized schedules derived here are used in
a fully deployed production system to recommend posting times for millions of
users every day.Comment: 10 pages, to appear in KDD201
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