18,383 research outputs found
Estimating Emotion Contagion on Social Media via Localized Diffusion in Dynamic Graphs
We present a computational approach for estimating emotion contagion on
social media networks. Built on a foundation of psychology literature, our
approach estimates the degree to which the perceivers' emotional states
(positive or negative) start to match those of the expressors, based on the
latter's content. We use a combination of deep learning and social network
analysis to model emotion contagion as a diffusion process in dynamic social
network graphs, taking into consideration key aspects like causality,
homophily, and interference. We evaluate our approach on user behavior data
obtained from a popular social media platform for sharing short videos. We
analyze the behavior of 48 users over a span of 8 weeks (over 200k audio-visual
short posts analyzed) and estimate how contagious the users with whom they
engage with are on social media. As per the theory of diffusion, we account for
the videos a user watches during this time (inflow) and the daily engagements;
liking, sharing, downloading or creating new videos (outflow) to estimate
contagion. To validate our approach and analysis, we obtain human feedback on
these 48 social media platform users with an online study by collecting
responses of about 150 participants. We report users who interact with more
number of creators on the platform are 12% less prone to contagion, and those
who consume more content of `negative' sentiment are 23% more prone to
contagion. We will publicly release our code upon acceptance
Reach and speed of judgment propagation in the laboratory
In recent years, a large body of research has demonstrated that judgments and
behaviors can propagate from person to person. Phenomena as diverse as
political mobilization, health practices, altruism, and emotional states
exhibit similar dynamics of social contagion. The precise mechanisms of
judgment propagation are not well understood, however, because it is difficult
to control for confounding factors such as homophily or dynamic network
structures. We introduce a novel experimental design that renders possible the
stringent study of judgment propagation. In this design, experimental chains of
individuals can revise their initial judgment in a visual perception task after
observing a predecessor's judgment. The positioning of a very good performer at
the top of a chain created a performance gap, which triggered waves of judgment
propagation down the chain. We evaluated the dynamics of judgment propagation
experimentally. Despite strong social influence within pairs of individuals,
the reach of judgment propagation across a chain rarely exceeded a social
distance of three to four degrees of separation. Furthermore, computer
simulations showed that the speed of judgment propagation decayed exponentially
with the social distance from the source. We show that information distortion
and the overweighting of other people's errors are two individual-level
mechanisms hindering judgment propagation at the scale of the chain. Our
results contribute to the understanding of social contagion processes, and our
experimental method offers numerous new opportunities to study judgment
propagation in the laboratory
Studying Paths of Participation in Viral Diffusion Process
Authors propose a conceptual model of participation in viral diffusion
process composed of four stages: awareness, infection, engagement and action.
To verify the model it has been applied and studied in the virtual social chat
environment settings. The study investigates the behavioral paths of actions
that reflect the stages of participation in the diffusion and presents
shortcuts, that lead to the final action, i.e. the attendance in a virtual
event. The results show that the participation in each stage of the process
increases the probability of reaching the final action. Nevertheless, the
majority of users involved in the virtual event did not go through each stage
of the process but followed the shortcuts. That suggests that the viral
diffusion process is not necessarily a linear sequence of human actions but
rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social
Informatics, SocInfo 201
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
Information is not a Virus, and Other Consequences of Human Cognitive Limits
The many decisions people make about what to pay attention to online shape
the spread of information in online social networks. Due to the constraints of
available time and cognitive resources, the ease of discovery strongly impacts
how people allocate their attention to social media content. As a consequence,
the position of information in an individual's social feed, as well as explicit
social signals about its popularity, determine whether it will be seen, and the
likelihood that it will be shared with followers. Accounting for these
cognitive limits simplifies mechanics of information diffusion in online social
networks and explains puzzling empirical observations: (i) information
generally fails to spread in social media and (ii) highly connected people are
less likely to re-share information. Studies of information diffusion on
different social media platforms reviewed here suggest that the interplay
between human cognitive limits and network structure differentiates the spread
of information from other social contagions, such as the spread of a virus
through a population.Comment: accepted for publication in Future Interne
The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks
We present new empirical evidence, based on millions of interactions on
Twitter, confirming that human contacts scale with population sizes. We
integrate such observations into a reaction-diffusion metapopulation framework
providing an analytical expression for the global invasion threshold of a
contagion process. Remarkably, the scaling of human contacts is found to
facilitate the spreading dynamics. Our results show that the scaling properties
of human interactions can significantly affect dynamical processes mediated by
human contacts such as the spread of diseases, and ideas
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