2,872 research outputs found
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
Online social media are complementing and in some cases replacing
person-to-person social interaction and redefining the diffusion of
information. In particular, microblogs have become crucial grounds on which
public relations, marketing, and political battles are fought. We introduce an
extensible framework that will enable the real-time analysis of meme diffusion
in social media by mining, visualizing, mapping, classifying, and modeling
massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect
astroturfing, smear campaigns, and other misinformation in the context of U.S.
political elections. We present some cases of abusive behaviors uncovered by
our service. Finally, we discuss promising preliminary results on the detection
of suspicious memes via supervised learning based on features extracted from
the topology of the diffusion networks, sentiment analysis, and crowdsourced
annotations
Clustering Memes in Social Media
The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 201
Inheritance patterns in citation networks reveal scientific memes
Memes are the cultural equivalent of genes that spread across human culture
by means of imitation. What makes a meme and what distinguishes it from other
forms of information, however, is still poorly understood. Our analysis of
memes in the scientific literature reveals that they are governed by a
surprisingly simple relationship between frequency of occurrence and the degree
to which they propagate along the citation graph. We propose a simple
formalization of this pattern and we validate it with data from close to 50
million publication records from the Web of Science, PubMed Central, and the
American Physical Society. Evaluations relying on human annotators, citation
network randomizations, and comparisons with several alternative approaches
confirm that our formula is accurate and effective, without a dependence on
linguistic or ontological knowledge and without the application of arbitrary
thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical
Review
Timescales of Massive Human Entrainment
The past two decades have seen an upsurge of interest in the collective
behaviors of complex systems composed of many agents entrained to each other
and to external events. In this paper, we extend concepts of entrainment to the
dynamics of human collective attention. We conducted a detailed investigation
of the unfolding of human entrainment - as expressed by the content and
patterns of hundreds of thousands of messages on Twitter - during the 2012 US
presidential debates. By time locking these data sources, we quantify the
impact of the unfolding debate on human attention. We show that collective
social behavior covaries second-by-second to the interactional dynamics of the
debates: A candidate speaking induces rapid increases in mentions of his name
on social media and decreases in mentions of the other candidate. Moreover,
interruptions by an interlocutor increase the attention received. We also
highlight a distinct time scale for the impact of salient moments in the
debate: Mentions in social media start within 5-10 seconds after the moment;
peak at approximately one minute; and slowly decay in a consistent fashion
across well-known events during the debates. Finally, we show that public
attention after an initial burst slowly decays through the course of the
debates. Thus we demonstrate that large-scale human entrainment may hold across
a number of distinct scales, in an exquisitely time-locked fashion. The methods
and results pave the way for careful study of the dynamics and mechanisms of
large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version
revised according to peer reviewers' comments: more detailed explanation of
the methods, and grounding of the hypothese
Structure and Dynamics of Information Pathways in Online Media
Diffusion of information, spread of rumors and infectious diseases are all
instances of stochastic processes that occur over the edges of an underlying
network. Many times networks over which contagions spread are unobserved, and
such networks are often dynamic and change over time. In this paper, we
investigate the problem of inferring dynamic networks based on information
diffusion data. We assume there is an unobserved dynamic network that changes
over time, while we observe the results of a dynamic process spreading over the
edges of the network. The task then is to infer the edges and the dynamics of
the underlying network.
We develop an on-line algorithm that relies on stochastic convex optimization
to efficiently solve the dynamic network inference problem. We apply our
algorithm to information diffusion among 3.3 million mainstream media and blog
sites and experiment with more than 179 million different pieces of information
spreading over the network in a one year period. We study the evolution of
information pathways in the online media space and find interesting insights.
Information pathways for general recurrent topics are more stable across time
than for on-going news events. Clusters of news media sites and blogs often
emerge and vanish in matter of days for on-going news events. Major social
movements and events involving civil population, such as the Libyan's civil war
or Syria's uprise, lead to an increased amount of information pathways among
blogs as well as in the overall increase in the network centrality of blogs and
social media sites.Comment: To Appear at the 6th International Conference on Web Search and Data
Mining (WSDM '13
Competition and Success in the Meme Pool: a Case Study on Quickmeme.com
The advent of social media has provided data and insights about how people
relate to information and culture. While information is composed by bits and
its fundamental building bricks are relatively well understood, the same cannot
be said for culture. The fundamental cultural unit has been defined as a
"meme". Memes are defined in literature as specific fundamental cultural
traits, that are floating in their environment together. Just like genes
carried by bodies, memes are carried by cultural manifestations like songs,
buildings or pictures. Memes are studied in their competition for being
successfully passed from one generation of minds to another, in different ways.
In this paper we choose an empirical approach to the study of memes. We
downloaded data about memes from a well-known website hosting hundreds of
different memes and thousands of their implementations. From this data, we
empirically describe the behavior of these memes. We statistically describe
meme occurrences in our dataset and we delineate their fundamental traits,
along with those traits that make them more or less apt to be successful
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