24,910 research outputs found
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks
Identifying the most influential spreaders that maximize information flow is
a central question in network theory. Recently, a scalable method called
"Collective Influence (CI)" has been put forward through collective influence
maximization. In contrast to heuristic methods evaluating nodes' significance
separately, CI method inspects the collective influence of multiple spreaders.
Despite that CI applies to the influence maximization problem in percolation
model, it is still important to examine its efficacy in realistic information
spreading. Here, we examine real-world information flow in various social and
scientific platforms including American Physical Society, Facebook, Twitter and
LiveJournal. Since empirical data cannot be directly mapped to ideal
multi-source spreading, we leverage the behavioral patterns of users extracted
from data to construct "virtual" information spreading processes. Our results
demonstrate that the set of spreaders selected by CI can induce larger scale of
information propagation. Moreover, local measures as the number of connections
or citations are not necessarily the deterministic factors of nodes' importance
in realistic information spreading. This result has significance for rankings
scientists in scientific networks like the APS, where the commonly used number
of citations can be a poor indicator of the collective influence of authors in
the community.Comment: 11 pages, 4 figure
Predicting Rising Follower Counts on Twitter Using Profile Information
When evaluating the cause of one's popularity on Twitter, one thing is
considered to be the main driver: Many tweets. There is debate about the kind
of tweet one should publish, but little beyond tweets. Of particular interest
is the information provided by each Twitter user's profile page. One of the
features are the given names on those profiles. Studies on psychology and
economics identified correlations of the first name to, e.g., one's school
marks or chances of getting a job interview in the US. Therefore, we are
interested in the influence of those profile information on the follower count.
We addressed this question by analyzing the profiles of about 6 Million Twitter
users. All profiles are separated into three groups: Users that have a first
name, English words, or neither of both in their name field. The assumption is
that names and words influence the discoverability of a user and subsequently
his/her follower count. We propose a classifier that labels users who will
increase their follower count within a month by applying different models based
on the user's group. The classifiers are evaluated with the area under the
receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy,
NY, US
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