23,264 research outputs found
Happiness is assortative in online social networks
Social networks tend to disproportionally favor connections between
individuals with either similar or dissimilar characteristics. This propensity,
referred to as assortative mixing or homophily, is expressed as the correlation
between attribute values of nearest neighbour vertices in a graph. Recent
results indicate that beyond demographic features such as age, sex and race,
even psychological states such as "loneliness" can be assortative in a social
network. In spite of the increasing societal importance of online social
networks it is unknown whether assortative mixing of psychological states takes
place in situations where social ties are mediated solely by online networking
services in the absence of physical contact. Here, we show that general
happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6
month record of their individual tweets, is indeed assortative across the
Twitter social network. To our knowledge this is the first result that shows
assortative mixing in online networks at the level of SWB. Our results imply
that online social networks may be equally subject to the social mechanisms
that cause assortative mixing in real social networks and that such assortative
mixing takes place at the level of SWB. Given the increasing prevalence of
online social networks, their propensity to connect users with similar levels
of SWB may be an important instrument in better understanding how both positive
and negative sentiments spread through online social ties. Future research may
focus on how event-specific mood states can propagate and influence user
behavior in "real life".Comment: 17 pages, 9 figure
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Forecasting audience increase on YouTube
User profiles constructed on Social Web platforms are often motivated by the need to maximise user reputation within a community. Subscriber, or follower, counts are an indicator of the influence and standing that the user has, where greater values indicate a greater perception or regard for what the user has to say or share. However, at present there lacks an understanding of the factors that lead to an increase in such audience levels, and how a user’s behaviour can a!ect their reputation. In this paper we attempt to fill this gap, by examining data collected from YouTube over regular time intervals. We explore the correlation between the subscriber counts and several behaviour features - extracted from both the user’s profile and the content they have shared. Through the use of a Multiple Linear Regression model we are able to forecast the audience levels that users will yield based on observed behaviour. Combining such a model with an exhaustive feature selection process, we yield statistically significant performance over a baseline model containing all features
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
Events in social networks : a stochastic actor-oriented framework for dynamic event processes in social networks
Interactions between people are ubiquitous. When people make phone calls, transfer money, connect on social network sites, or visit each other, these actions can be collected as dyadic, directed, relational events. Each of those events can be understood as driven by multiple individual decisions that at least partially involve rational considerations. This book aims at developing models that allow to understand individual event decisions in the context of large social networks
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