11,445 research outputs found
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
This study is a first, exploratory attempt to use quantitative semantics
techniques and topological analysis to analyze systemic patterns arising in a
complex political system. In particular, we use a rich data set covering all
speeches and debates in the UK House of Commons between 1975 and 2014. By the
use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show
that both members and parties feature specific roles within the system,
consistent over time, and extract global patterns indicating levels of
political cohesion. Our results provide a wide array of novel hypotheses about
the complex dynamics of political systems, with valuable policy applications
Semantic Web Tools and Decision-Making
Semantic Web technologies are intertwined with decision-making
processes. In this paper the general objectives of the semantic web tools are reviewed
and characterized, as well as the categories of decision support tools, in
order to establish an intersection of utility and use. We also elaborate on actual
and foreseen possibilities for a deeper integration, considering the actual implementation,
opportunities and constraints in the decision-making context.info:eu-repo/semantics/publishedVersio
Programmable Insight: A Computational Methodology to Explore Online News Use of Frames
abstract: The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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