101 research outputs found
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
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
Hot Streaks on Social Media
Measuring the impact and success of human performance is common in various
disciplines, including art, science, and sports. Quantifying impact also plays
a key role on social media, where impact is usually defined as the reach of a
user's content as captured by metrics such as the number of views, likes,
retweets, or shares. In this paper, we study entire careers of Twitter users to
understand properties of impact. We show that user impact tends to have certain
characteristics: First, impact is clustered in time, such that the most
impactful tweets of a user appear close to each other. Second, users commonly
have 'hot streaks' of impact, i.e., extended periods of high-impact tweets.
Third, impact tends to gradually build up before, and fall off after, a user's
most impactful tweet. We attempt to explain these characteristics using various
properties measured on social media, including the user's network, content,
activity, and experience, and find that changes in impact are associated with
significant changes in these properties. Our findings open interesting avenues
for future research on virality and influence on social media.Comment: Accepted as a full paper at ICWSM 2019. Please cite the ICWSM versio
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