59,782 research outputs found

    Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

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    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

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    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

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    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

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    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

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    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
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