1,164 research outputs found
Can Cascades be Predicted?
On many social networking web sites such as Facebook and Twitter, resharing
or reposting functionality allows users to share others' content with their own
friends or followers. As content is reshared from user to user, large cascades
of reshares can form. While a growing body of research has focused on analyzing
and characterizing such cascades, a recent, parallel line of work has argued
that the future trajectory of a cascade may be inherently unpredictable. In
this work, we develop a framework for addressing cascade prediction problems.
On a large sample of photo reshare cascades on Facebook, we find strong
performance in predicting whether a cascade will continue to grow in the
future. We find that the relative growth of a cascade becomes more predictable
as we observe more of its reshares, that temporal and structural features are
key predictors of cascade size, and that initially, breadth, rather than depth
in a cascade is a better indicator of larger cascades. This prediction
performance is robust in the sense that multiple distinct classes of features
all achieve similar performance. We also discover that temporal features are
predictive of a cascade's eventual shape. Observing independent cascades of the
same content, we find that while these cascades differ greatly in size, we are
still able to predict which ends up the largest
Characterizing Attention Cascades in WhatsApp Groups
An important political and social phenomena discussed in several countries,
like India and Brazil, is the use of WhatsApp to spread false or misleading
content. However, little is known about the information dissemination process
in WhatsApp groups. Attention affects the dissemination of information in
WhatsApp groups, determining what topics or subjects are more attractive to
participants of a group. In this paper, we characterize and analyze how
attention propagates among the participants of a WhatsApp group. An attention
cascade begins when a user asserts a topic in a message to the group, which
could include written text, photos, or links to articles online. Others then
propagate the information by responding to it. We analyzed attention cascades
in more than 1.7 million messages posted in 120 groups over one year. Our
analysis focused on the structural and temporal evolution of attention cascades
as well as on the behavior of users that participate in them. We found specific
characteristics in cascades associated with groups that discuss political
subjects and false information. For instance, we observe that cascades with
false information tend to be deeper, reach more users, and last longer in
political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science
Conference (WebSci 2019). Please cite the WebSci versio
The Lifecycle and Cascade of WeChat Social Messaging Groups
Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th
International World Wide Web Conference (WWW 2016
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
Recently, many online social networks, such as MySpace, Orkut, and
Friendster, have faced inactivity decay of their members, which contributed to
the collapse of these networks. The reasons, mechanics, and prevention
mechanisms of such inactivity decay are not fully understood. In this work, we
analyze decayed and alive sub-websites from the StackExchange platform. The
analysis mainly focuses on the inactivity cascades that occur among the members
of these communities. We provide measures to understand the decay process and
statistical analysis to extract the patterns that accompany the inactivity
decay. Additionally, we predict cascade size and cascade virality using machine
learning. The results of this work include a statistically significant
difference of the decay patterns between the decayed and the alive
sub-websites. These patterns are mainly: cascade size, cascade virality,
cascade duration, and cascade similarity. Additionally, the contributed
prediction framework showed satisfactory prediction results compared to a
baseline predictor. Supported by empirical evidence, the main findings of this
work are: (1) the decay process is not governed by only one network measure; it
is better described using multiple measures; (2) the expert members of the
StackExchange sub-websites were mainly responsible for the activity or
inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is
going through decay dynamics that may lead to it becoming fully-decayed; and
(4) decayed sub-websites were originally less resilient to inactivity decay,
unlike the alive sub-websites
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