@conference {12413,
	title = {Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {734 - 737},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Current events such as the Park51 Project in downtown Manhattan create "critical discourse moments," explosions of discourse around a topic that can be exploited for data gathering. Policymakers have a need to understand the dynamics of public discussion in real time. Human values, which are cognitively related to attitudes and serve as reference points in moral argument, are important indicators of what{\textquoteright}s at stake in a public controversy. This work shows that it is possible to link values data with reader behavior to infer values implicit in a topical corpus, and that it is possible to automate this process using machine learning.},
	keywords = {audience simulation, behavioural sciences computing, crowdsourcing, Educational institutions, human values, HUMANS, learning (artificial intelligence), machine learning, moral argument, natural language processing, Park51 project, Presses, public controversy, public discussion, Security, social sciences computing, support vector machines, Weaving},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.238},
	author = {Templeton,T.C. and Fleischmann,K.R. and Jordan Boyd-Graber}
}
@article {18899,
	title = {Computational Cultural Dynamics},
	journal = {Intelligent Systems, IEEE},
	volume = {23},
	year = {2008},
	month = {2008/08//july},
	pages = {18 - 19},
	abstract = {Computational cultural dynamics blends the behavioral and social sciences - fields such as political science, psychology, journalism, anthropology, and sociology - with technological fields such as computer science, computational linguistics, game theory, statistical modeling, and operations research.},
	keywords = {anthropology, behavioral science, behavioural sciences computing, computational cultural dynamics, computational linguistics, Computer science, game theory, journalism, operations research, political science, Psychology, social sciences, social sciences computing, sociology, statistical modeling},
	isbn = {1541-1672},
	doi = {10.1109/MIS.2008.61},
	author = {Nau, Dana S. and Wilkenfeld,J.}
}
@article {16983,
	title = {Balancing Systematic and Flexible Exploration of Social Networks},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {693 - 700},
	abstract = {Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks},
	keywords = {Aggregates, algorithms, attribute ranking, Cluster Analysis, Computer Graphics, Computer simulation, Coordinate measuring machines, coordinated views, Data analysis, data visualisation, Data visualization, exploratory data analysis, Filters, Gain measurement, graph theory, Graphical user interfaces, Information Storage and Retrieval, interactive graph visualization, matrix algebra, matrix overview, Models, Biological, Navigation, network visualization, Pattern analysis, Population Dynamics, Social Behavior, social network analysis, Social network services, social networks, social sciences computing, Social Support, SocialAction, software, statistical analysis, statistical methods, User-Computer Interface},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.122},
	author = {Perer,A. and Shneiderman, Ben}
}
