4,870 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
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#Bigbirds never die: Understanding social dynamics of emergent hashtag
We examine the growth, survival, and context of 256 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: “winners” that emerge more quickly and are sustained for longer periods of time than other “also-rans” hashtags. We propose a “conversational vibrancy” framework to capture dynamics of hashtags based on their topicality, interactivity, diversity, and prominence. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between features of this framework and the relative success of hashtags. Specifically, retweets always contribute to faster hashtag adoption, replies extend the life of “winners” while having no effect on “also-rans.” This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks. We draw on theories of uses and gratification, organizational ecology, and language evolution to discuss these findings and their implications for understanding social influence and collective action in social media more generally
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
Infectivity Enhances Prediction of Viral Cascades in Twitter
Models of contagion dynamics, originally developed for infectious diseases,
have proven relevant to the study of information, news, and political opinions
in online social systems. Modelling diffusion processes and predicting viral
information cascades are important problems in network science. Yet, many
studies of information cascades neglect the variation in infectivity across
different pieces of information. Here, we employ early-time observations of
online cascades to estimate the infectivity of distinct pieces of information.
Using simulations and data from real-world Twitter retweets, we demonstrate
that these estimated infectivities can be used to improve predictions about the
virality of an information cascade. Developing our simulations to mimic the
real-world data, we consider the effect of the limited effective time for
transmission of a cascade and demonstrate that a simple model for slow but
non-negligible decay of the infectivity captures the essential properties of
retweet distributions. These results demonstrate the interplay between the
intrinsic infectivity of a tweet and the complex network environment within
which it diffuses, strongly influencing the likelihood of becoming a viral
cascade.Comment: 16 pages, 10 figure
#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds
Compounding of natural language units is a very common phenomena. In this
paper, we show, for the first time, that Twitter hashtags which, could be
considered as correlates of such linguistic units, undergo compounding. We
identify reasons for this compounding and propose a prediction model that can
identify with 77.07% accuracy if a pair of hashtags compounding in the near
future (i.e., 2 months after compounding) shall become popular. At longer times
T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This
technique has strong implications to trending hashtag recommendation since
newly formed hashtag compounds can be recommended early, even before the
compounding has taken place. Further, humans can predict compounds with an
overall accuracy of only 48.7% (treated as baseline). Notably, while humans can
discriminate the relatively easier cases, the automatic framework is successful
in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported
Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM
conference on Computer-Supported Cooperative Work and Social Computing (CSCW
2016
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An overview study of twitter in the UK local government
Copyright @ 2012 Brunel UniversityMicroblogging applications are becoming a momentous element of the public sector social media agenda. The potential of Twitter to update the public with frequent, concise and real-time content has motivated many pubic authorities to create their accounts, thus generating an interesting topic for research. This paper seeks to make an empirical and methodological contribution to this new body of knowledge by presenting an overview study of general Twitter accounts maintained by UK local government authorities. Over 296,000 tweets were collected from the 187officially listed local government accounts. The analysis was conducted in two stages: an examination of the Twitter networks developed by the accounts was followed by a structural analysis of the tweets. The combination of online research and social media analytics techniques enabled us to reach important conclusions about the use of Twitter by those authorities. The findings indicate high level of maturity of Twitter in the UK local government and point to several directions for further increasing the impact and visibility of those accounts within a social media strategy
Traveling Trends: Social Butterflies or Frequent Fliers?
Trending topics are the online conversations that grab collective attention
on social media. They are continually changing and often reflect exogenous
events that happen in the real world. Trends are localized in space and time as
they are driven by activity in specific geographic areas that act as sources of
traffic and information flow. Taken independently, trends and geography have
been discussed in recent literature on online social media; although, so far,
little has been done to characterize the relation between trends and geography.
Here we investigate more than eleven thousand topics that trended on Twitter in
63 main US locations during a period of 50 days in 2013. This data allows us to
study the origins and pathways of trends, how they compete for popularity at
the local level to emerge as winners at the country level, and what dynamics
underlie their production and consumption in different geographic areas. We
identify two main classes of trending topics: those that surface locally,
coinciding with three different geographic clusters (East coast, Midwest and
Southwest); and those that emerge globally from several metropolitan areas,
coinciding with the major air traffic hubs of the country. These hubs act as
trendsetters, generating topics that eventually trend at the country level, and
driving the conversation across the country. This poses an intriguing
conjecture, drawing a parallel between the spread of information and diseases:
Do trends travel faster by airplane than over the Internet?Comment: Proceedings of the first ACM conference on Online social networks,
pp. 213-222, 201
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