4,719 research outputs found
Quantifying Triadic Closure in Multi-Edge Social Networks
Multi-edge networks capture repeated interactions between individuals. In
social networks, such edges often form closed triangles, or triads. Standard
approaches to measure this triadic closure, however, fail for multi-edge
networks, because they do not consider that triads can be formed by edges of
different multiplicity. We propose a novel measure of triadic closure for
multi-edge networks of social interactions based on a shared partner statistic.
We demonstrate that our operalization is able to detect meaningful closure in
synthetic and empirical multi-edge networks, where common approaches fail. This
is a cornerstone in driving inferential network analyses from the analysis of
binary networks towards the analyses of multi-edge and weighted networks, which
offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is
generated by autonomous entities known as social bots. In this work we present
a framework to detect such entities on Twitter. We leverage more than a
thousand features extracted from public data and meta-data about users:
friends, tweet content and sentiment, network patterns, and activity time
series. We benchmark the classification framework by using a publicly available
dataset of Twitter bots. This training data is enriched by a manually annotated
collection of active Twitter users that include both humans and bots of varying
sophistication. Our models yield high accuracy and agreement with each other
and can detect bots of different nature. Our estimates suggest that between 9%
and 15% of active Twitter accounts are bots. Characterizing ties among
accounts, we observe that simple bots tend to interact with bots that exhibit
more human-like behaviors. Analysis of content flows reveals retweet and
mention strategies adopted by bots to interact with different target groups.
Using clustering analysis, we characterize several subclasses of accounts,
including spammers, self promoters, and accounts that post content from
connected applications.Comment: Accepted paper for ICWSM'17, 10 pages, 8 figures, 1 tabl
Understanding and Predicting Delay in Reciprocal Relations
Reciprocity in directed networks points to user's willingness to return
favors in building mutual interactions. High reciprocity has been widely
observed in many directed social media networks such as following relations in
Twitter and Tumblr. Therefore, reciprocal relations between users are often
regarded as a basic mechanism to create stable social ties and play a crucial
role in the formation and evolution of networks. Each reciprocity relation is
formed by two parasocial links in a back-and-forth manner with a time delay.
Hence, understanding the delay can help us gain better insights into the
underlying mechanisms of network dynamics. Meanwhile, the accurate prediction
of delay has practical implications in advancing a variety of real-world
applications such as friend recommendation and marketing campaign. For example,
by knowing when will users follow back, service providers can focus on the
users with a potential long reciprocal delay for effective targeted marketing.
This paper presents the initial investigation of the time delay in reciprocal
relations. Our study is based on a large-scale directed network from Tumblr
that consists of 62.8 million users and 3.1 billion user following relations
with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal
a number of interesting patterns about the delay that motivate the development
of a principled learning model to predict the delay in reciprocal relations.
Experimental results on the above mentioned dynamic networks corroborate the
effectiveness of the proposed delay prediction model.Comment: 10 page
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