23 research outputs found
Network Weirdness: Exploring the Origins of Network Paradoxes
Social networks have many counter-intuitive properties, including the
"friendship paradox" that states, on average, your friends have more friends
than you do. Recently, a variety of other paradoxes were demonstrated in online
social networks. This paper explores the origins of these network paradoxes.
Specifically, we ask whether they arise from mathematical properties of the
networks or whether they have a behavioral origin. We show that sampling from
heavy-tailed distributions always gives rise to a paradox in the mean, but not
the median. We propose a strong form of network paradoxes, based on utilizing
the median, and validate it empirically using data from two online social
networks. Specifically, we show that for any user the majority of user's
friends and followers have more friends, followers, etc. than the user, and
that this cannot be explained by statistical properties of sampling. Next, we
explore the behavioral origins of the paradoxes by using the shuffle test to
remove correlations between node degrees and attributes. We find that paradoxes
for the mean persist in the shuffled network, but not for the median. We
demonstrate that strong paradoxes arise due to the assortativity of user
attributes, including degree, and correlation between degree and attribute.Comment: Accepted to ICWSM 201
Twitter session analytics: profiling susers' short-term behavioral changes
[Proceeding of]: 8th International Conference (SocInfo 2016), Bellevue, WA, USA, November 11-14, 2016.Human behavior shows strong daily, weekly, and monthlypatterns. In this work, we demonstrate online behavioral changes thatoccur on a much smaller time scale: minutes, rather than days or weeks.Specifically, we study how people distribute their effort over differenttasks during periods of activity on the Twitter social platform. Wedemonstrate that later in a session on Twitter, people prefer to perform simpler tasks, such as replying and retweeting others' posts, ratherthan composing original messages, and they also tend to post shortermessages. We measure the strength of this effect empirically and statistically using mixed-effects models, and find that the first post of a sessionis up to 25 % more likely to be a composed message, and 10-20 % lesslikely to be a reply or retweet. Qualitatively, our results hold for differentpopulations of Twitter users segmented by how active and well-connectedthey are. Although our work does not resolve the mechanisms responsible for these behavioral changes, our results offer insights for improvinguser experience and engagement on online social platforms.Publicad
Friendship Paradox Redux: Your Friends Are More Interesting Than You
Feld's friendship paradox states that "your friends have more friends than
you, on average." This paradox arises because extremely popular people, despite
being rare, are overrepresented when averaging over friends. Using a sample of
the Twitter firehose, we confirm that the friendship paradox holds for >98% of
Twitter users. Because of the directed nature of the follower graph on Twitter,
we are further able to confirm more detailed forms of the friendship paradox:
everyone you follow or who follows you has more friends and followers than you.
This is likely caused by a correlation we demonstrate between Twitter activity,
number of friends, and number of followers. In addition, we discover two new
paradoxes: the virality paradox that states "your friends receive more viral
content than you, on average," and the activity paradox, which states "your
friends are more active than you, on average." The latter paradox is important
in regulating online communication. It may result in users having difficulty
maintaining optimal incoming information rates, because following additional
users causes the volume of incoming tweets to increase super-linearly. While
users may compensate for increased information flow by increasing their own
activity, users become information overloaded when they receive more
information than they are able or willing to process. We compare the average
size of cascades that are sent and received by overloaded and underloaded
users. And we show that overloaded users post and receive larger cascades and
they are poor detector of small cascades.Comment: Accepted to ICWSM 201
Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
Randomized experiments, or "A/B" tests, remain the gold standard for
evaluating the causal effect of a policy intervention or product change.
However, experimental settings, such as social networks, where users are
interacting and influencing one another, may violate conventional assumptions
of no interference for credible causal inference. Existing solutions to the
network setting include accounting for the fraction or count of treated
neighbors in a user's network, yet most current methods do not account for the
local network structure beyond simply counting the number of neighbors. Our
study provides an approach that accounts for both the local structure in a
user's social network via motifs as well as the treatment assignment conditions
of neighbors. We propose a two-part approach. We first introduce and employ
"causal network motifs", which are network motifs that characterize the
assignment conditions in local ego networks; and then we propose a tree-based
algorithm for identifying different network interference conditions and
estimating their average potential outcomes. Our approach can account for
social network theories, such as structural diversity and echo chambers, and
also can help specify network interference conditions that are suitable to each
experiment. We test our method on a synthetic network setting and on a
real-world experiment on a large-scale network, which highlight how accounting
for local structures can better account for different interference patterns in
networks.Comment: 12 pages; to appear in the Web Conference (WWW) 202