2,646 research outputs found

    Discourse network analysis: policy debates as dynamic networks

    Get PDF
    Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed

    Major league baseball fans’ climate change attitudes and willingness to adapt: climate vulnerability vs. America’s pastime.

    Get PDF
    Climate change threatens the ability to enjoy sport around the world, including in the United States. While the scientific community reached consensus regarding the presence and severity of climate change near the turn of the twenty-first century, that same agreement has not been met across the American general public. Major League Baseball (MLB) is particularly vulnerable to climate change in the U.S. due to its season duration, geographic footprint, and largely outdoor nature. Therefore, the purpose of this study was to investigate relationships between U.S.-based MLB fans’ sport identification and their climate change attitudes, perceptions of climate change risk, and willingness to adapt. Specifically, this study sought to advance climate change perception research by focusing on sport fans in a sport context, groups that are understudied in climate change and sport ecology research. Using social identity theory to frame the significance of sport identification, this study aimed to model transitions from cognition to action for MLB fans. Social identity theory served to explain how an individual creates meaning about the world around them, in this instance climate change, by the social groups to which they voluntarily belong, that is sport identification. A cross-sectional survey design was used to address the study’s purpose. The questionnaire was designed and hosted on Qualtrics Survey Software, but distributed as a Human Intelligence Task on Amazon’s Mechanical Turk. The questionnaire contained items to measure fans’ attitudes, general risk perceptions, sport-specific risk perceptions, and willingness to adapt. Participant responses (n = 540) indicated personal experiences with extreme weather most strongly influenced general climate change risk perceptions. Further, responses revealed fans who had general climate change risk perceptions were more likely to have sport-specific risk perceptions. This relationship was not moderated by sport identification, but sport identification did significantly predict sport-specific risk perceptions. Likewise, sport identification did not moderate the relationship between fans’ sport-specific climate change risk perceptions and their willingness to adapt. However, responses revealed fans who perceived climate change risks to the sport were more willing to adapt their behaviors to climate change. As a result of these findings, there were several theoretical and practical implications. Theoretically, although sport identification did not moderate the hypothesized relationships, social identity theory does serve as an avenue to explore the connections between sport fans and the realities of climate change on sport. The overall model structure was supported, indicating the possibility to examine found relationships through additional theoretical lenses. The findings revealed a direct connection between sport consumer behavior research and climate change, opening new avenues for researchers within sport management and climate research. From a practical standpoint, this study found early empirical evidence to support the United Nations’ suggestion that sport fans are critical to engaging in, and accelerating, climate action in the sport sector. Additionally, this study’s findings suggest pro-environmental efforts pertaining to climate adaptation in MLB should include fans, and the UN should invest in educational awareness regarding climate change risks to sport for fans

    Influencing interaction: Development of the design with intent method

    Get PDF
    Persuasive Technology has the potential to influence user behavior for social benefit, e.g. to reduce environmental impact, but designers are lacking guidance choosing among design techniques for influencing interaction. The Design with Intent Method, a ‘suggestion tool’ addressing this problem, is introduced in this paper, and applied to the briefs of reducing unnecessary household lighting use, and improving the efficiency of printing, primarily to evaluate the method’s usability and guide the direction of its development. The trial demonstrates that the DwI Method is quick to apply and leads to a range of relevant design concepts. With development, the DwI Method could be a useful tool for designers working on influencing user behavior

    Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

    Get PDF
    abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

    Understanding climate change risks to the United States military

    Full text link
    The Department of Defense (DoD) has acknowledged climate change as a risk national security. Ongoing impacts include the loss of training and operational sites to climate hazards. Operationally, conflict and natural disasters around the world have been exacerbated by increasing heat, desertification, and flooding. Increasing average temperatures, the flagship issue of climate change, is a significant contributor to heat-illness in military personnel. This project explores the relationship between climate change and the U.S. military, ongoing efforts to evaluate and address the risk, and the overall impacts on training readiness. Measuring climate related vulnerability is a complex process. For the DoD to apply a common framework across a vast network of fundamentally different sites is an especially wicked problem. I recommend a tiered approach to iteratively narrow the focus and resources allocated to the most mission critical and at-risk sites. The process begins with a screening survey, continues to in-depth site-specific impact assessments, and ends with implementation of technical and institutional adaptations. Recent efforts by the DoD have not fully executed this process and resulting reports are resultingly insufficient. I identify a lack of consideration for heat stress on servicemembers. Using historical site data and projections, I determine that the risk of heat-illness and lost training time will increase. Leaders can use this data to plan risk mitigation efforts through changing training locations, timing, or control measures. The military must continue to adapt and overcome challenges of the coming century by using available scientific information to reduce risk during the planning process

    Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change

    Get PDF
    This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies

    Climate Change Projection and Time-varying Multi-dimensional Risk Analysis

    Get PDF
    In recent decades, population growth and global warming consequent to greenhouse gas emissions because of human activities, has changed the atmospheric composition leading to intensifying extreme climate phenomena and overall increase of extreme events. These extreme events have caused human suffering and devastating effects in recent record-breaking warming years. To mitigate adverse consequences arising from global warming, the best strategy is to project the future probabilistic behavior of extreme climate phenomena under changing environment. The first contribution of this research is to improve the predictive power of regression-based statistical downscaling processes to accurately project the future behavior of extreme climate phenomena. First, a supervised dimensionality reduction algorithm is proposed for the statistical downscaling to derive a low-dimensional manifold representing climate change signals encoding of high-dimensional atmospheric variables. Such an algorithm is novel in climate change studies as past literature has focused on deriving low-dimensional principal components from large-scale atmospheric projectors without taking into account the target hydro-climate variables. The new algorithm called Supervised Principal Component analysis (Supervised PCA) outperforms all of the existing state-of-the-art dimensionality reduction algorithms. The model improves the performance of the statistical downscaling modelling through deriving subspaces that have maximum dependency with the target hydro-climate variables. A kernel version of Supervised PCA is also introduced to reduce nonlinear dimensionality and capture all of the nonlinear and complex variabilities between hydro-climate response variable and atmospheric projectors. To address the biases arising from difference between observed and simulated large-scale atmospheric projectors, and to represent anomalies of low frequency variability of teleconnections in General Circulation Models (GCMs), a Multivariate Recursive Nesting Bias Correction (MRNBC) is proposed to the regression-based statistical downscaling. The proposed method is able to use multiple variables in multiple locations to simultaneously correct temporal and spatial biases in cross dependent multi-projectors. To reduce another source of uncertainty arising from complexity and nonlinearity in quantitative empirical relationships in the statistical downscaling, the results demonstrate the superiority of a Bayesian machine-learning algorithm. The predictive power of the statistical downscaling is therefore improved through addressing the aforementioned sources of uncertainty. This results in improvement of the projection of the global warming impacts on the probabilistic behavior of hydro-climate variables using future multi-model ensemble GCMs under forcing climate change scenarios. The results of two Design-of-Experiments also reveal that the proposed comprehensive statistical downscaling is credible and adjustable to the changes under non-stationary conditions arising from climate change. Under the impact of climate change arising from anthropogenic global warming, it is demonstrated that the nature and the risk of extreme climate phenomena are changed over time. It is also well known that the extreme climate processes are multi-dimensional by their very nature characterized by multi-dimensions that are highly dependent. Accordingly, to strength the reliability of infrastructure designs and the management of water systems in the changing climate, it is of crucial importance to update the risk concept to a new adaptive multi-dimensional time-varying one to integrate anomalies of dynamic anthropogenically forced environments. The main contribution of this research is to develop a new generation of multivariate time-varying risk concept for an adaptive design framework in non-stationary conditions arising from climate change. This research develops a Bayesian, dynamic conditional copula model describing time-varying dependence structure between mixed continuous and discrete marginals of extreme multi-dimensional climate phenomena. The framework is able to integrate any anomalies in extreme multi-dimensional events in non-stationary conditions arising from climate change. It generates iterative samples using a Markov Chain Monte Carlo (MCMC) method from the full conditional marginals and joint distribution in a fully likelihood-based Bayesian inference. The framework also introduces a fully Bayesian, time-varying Joint Return Period (JRP) concept to quantify the extent of changes in the nature and the risk of extreme multi-dimensional events over time under the impact of climate change. The proposed generalized time-dependent risk framework can be applied to all stochastic multi-dimensional climate systems that are under the influence of changing environments
    • …
    corecore