154 research outputs found
Mitigating Overexposure in Viral Marketing
In traditional models for word-of-mouth recommendations and viral marketing,
the objective function has generally been based on reaching as many people as
possible. However, a number of studies have shown that the indiscriminate
spread of a product by word-of-mouth can result in overexposure, reaching
people who evaluate it negatively. This can lead to an effect in which the
over-promotion of a product can produce negative reputational effects, by
reaching a part of the audience that is not receptive to it.
How should one make use of social influence when there is a risk of
overexposure? In this paper, we develop and analyze a theoretical model for
this process; we show how it captures a number of the qualitative phenomena
associated with overexposure, and for the main formulation of our model, we
provide a polynomial-time algorithm to find the optimal marketing strategy. We
also present simulations of the model on real network topologies, quantifying
the extent to which our optimal strategies outperform natural baselinesComment: In AAAI-1
Local Search in Unstructured Networks
We review a number of message-passing algorithms that can be used to search
through power-law networks. Most of these algorithms are meant to be
improvements for peer-to-peer file sharing systems, and some may also shed some
light on how unstructured social networks with certain topologies might
function relatively efficiently with local information. Like the networks that
they are designed for, these algorithms are completely decentralized, and they
exploit the power-law link distribution in the node degree. We demonstrate that
some of these search algorithms can work well on real Gnutella networks, scale
sub-linearly with the number of nodes, and may help reduce the network search
traffic that tends to cripple such networks.Comment: v2 includes minor revisions: corrections to Fig. 8's caption and
references. 23 pages, 10 figures, a review of local search strategies in
unstructured networks, a contribution to `Handbook of Graphs and Networks:
From the Genome to the Internet', eds. S. Bornholdt and H.G. Schuster
(Wiley-VCH, Berlin, 2002), to be publishe
The Structure of U.S. College Networks on Facebook
Anecdotally, social connections made in university have life-long impact. Yet
knowledge of social networks formed in college remains episodic, due in large
part to the difficulty and expense involved in collecting a suitable dataset
for comprehensive analysis. To advance and systematize insight into college
social networks, we describe a dataset of the largest online social network
platform used by college students in the United States. We combine
de-identified and aggregated Facebook data with College Scorecard data,
campus-level information provided by U.S. Department of Education, to produce a
dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and
universities, spanning 7.6 million students. To perform the difficult task of
comparing these networks of different sizes we develop a new methodology. We
compute features over sampled ego-graphs, train binary classifiers for every
pair of graphs, and operationalize distance between graphs as predictive
accuracy. Social networks of different year cohorts at the same school are
structurally more similar to one another than to cohorts at other schools.
Networks from similar schools have similar structures, with the public/private
and graduation rate dimensions being the most distinguishable. We also relate
school types to specific outcomes. For example, students at private schools
have larger networks that are more clustered and with higher homophily by year.
Our findings may help illuminate the role that colleges play in shaping social
networks which partly persist throughout people's lives.Comment: ICWSM-202
The Geography of Facebook Groups in the United States
We use exploratory factor analysis to investigate the online persistence of
known community-level patterns of social capital variance in the U.S. context.
Our analysis focuses on Facebook groups, specifically those that tend to
connect users in the same local area. We investigate the relationship between
established, localized measures of social capital at the county level and
patterns of participation in Facebook groups in the same areas. We identify
four main factors that distinguish Facebook group engagement by county. The
first captures small, private groups, dense with friendship connections. The
second captures very local and small groups. The third captures non-local,
large, public groups, with more age mixing. The fourth captures partially local
groups of medium to large size. The first and third factor correlate with
community level social capital measures, while the second and fourth do not.
Together and individually, the factors are predictive of offline social capital
measures, even controlling for various demographic attributes of the counties.
Our analysis reveals striking patterns of correlation between established
measures of social capital and patterns of online interaction in local Facebook
groups. To our knowledge this is the first systematic test of the association
between offline regional social capital and patterns of online community
engagement in the same regions.Comment: To be presented at AAAI ICWSM '23. Replication data is available at
https://doi.org/10.7910/DVN/OYQVE
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