59,782 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
Visualizing Social Networks to Inform Tactical Engagement Strategies that will Influence the Human Domain
The Special Operations Command, Marine Corps, and Army recently formed the Strategic Landpower Task Force to study
the confluence of the land, cyber, and human domains. To support the Task Force’s research, this paper demonstrates the
utility of visualizing social networks in order to inform a unit’s population tactical engagement strategy. We illustrate how
collecting, structuring and visualizing socio-cultural data can assist units to rapidly communicate human dynamics,
visualize community and group affiliations, prepare for key leader engagements, highlight potential powerbrokers, and
identify information gaps about the human terrain. We provide real world examples from a recent deployment to
Kandahar, Afghanistan. These examples reveal how social network and link analysis can assist units to understand and
influence the human domain at the tactical level
Extending adjacency matrices to 3D with triangles
Social networks are the fabric of society and the subject of frequent visual
analysis. Closed triads represent triangular relationships between three people
in a social network and are significant for understanding inherent
interconnections and influence within the network. The most common methods for
representing social networks (node-link diagrams and adjacency matrices) are
not optimal for understanding triangles. We propose extending the adjacency
matrix form to 3D for better visualization of network triads. We design a 3D
matrix reordering technique and implement an immersive interactive system to
assist in visualizing and analyzing closed triads in social networks. A user
study and usage scenarios demonstrate that our method provides substantial
added value over node-link diagrams in improving the efficiency and accuracy of
manipulating and understanding the social network triads.Comment: 10 pages, 8 figures and 3 table
SENTIMENT ANALYSIS OF SOCIAL NETWORKS AS A CHALLENGE TO THE DIGITAL MARKETING
Huge amounts of data, in the form of messages on social networks, represent a challange for digital marketing and marketing analytics when meeting the requirements, needs and customer satisfaction with services or products. Marketing strives to be a part of the overall culture based on the data and to define marketing strategies that respond to consumers and thus to provide economic benefits for the company. Therefore, the focus of marketing analysis is on the data recorded at the social networks. This paper shows one possible integration of information technology and data mining tools, with the goal of visualizing the attitudes and opinions on the social networks in the form of a word cloud, which can then further be used to create marketing strategies and improve customer relations and customer service.</p
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
The boundary coefficient : a vertex measure for visualizing and finding structure in weighted graphs
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