Tweeting the issues in the age of social media? intermedia agenda setting between The New York Times and Twitter

This dissertation examined the intermedia agenda setting relationship between the online publication of the New York Times (i.e., NYTimes.com) and Twitter. This relationship was examined within the context of the changing media environment. The news media industry is facing down questions about its ability to turn a profit and maintain significant audience share. Simultaneously, social media services such as Twitter are growing exponentially. To this end, this dissertation explored the relative influence of each media on the other in an age where some scholars are questioning the agenda setting role of traditional news media. The dissertation assesses the argument that social media, specifically Twitter, has a direct influence on the news media agenda. This dissertation tested several hypotheses which hold that there is bi-directional intermedia agenda setting between the New York Times and Twitter both over the course of a single day and between days. Two content analyses were conducted. Data were collected twice per day over the course of one week. One content analysis examined the content of the online publication of the New York Times. The second content analysis examined posts made to Twitter. Cross-lagged panels with the Rozelle-Campbell Baseline were used to assess the nature of the hypothesized relationship. Results of the cross-correlation showed a lack of intermedia agenda setting between the New York Times online publication and Twitter for both the within-day and between-day panels. Further, results showed a lack of intermedia agenda setting for specific issues examined: the economy, the military, national security, and terrorism. Results overall suggested that the nature of the relationship between the two media under study is one of subtle influence. These results raise additional issues about the agenda setting role of traditional news media extending this argument to the social media environment. Results also demonstrate that the news media agenda and social media agenda are often similar, questioning notions of audience fragmentation as a casualty of the news media s agenda setting ability. Results were discussed in terms of their implications for the field of agenda setting research, as well as limitations and directions for future research

Space lattice focusing: on the way to extremely low accelerated beam divergence

It is widely known the multiple channel acceleration is the most adequate way to save initial beam parameters due to the possibility of decreasing Coulomb forces in intensive input beams. To keep beam initial emittance and divergence for high enough specific value of the injection ion beam during acceleration the input beam should be split on multiple beams and every the micro beam must be screened from each other as much as possible. On the other hand, it is very much desirable to keep the total macro beam rather compact transversally and try to accelerate all the micro beams within the same accelerator structure at the same RF field. Attempts to use conventional quadruple focusing channels both RF and electrostatic for multiple beam acceleration usually lead to extremely complicate and bulky construction of the structure. We suppose multiple beam linac channels with alternating phase focusing (APF) as more adequate for the purpose while they are limited by less values of beam capture into acceleration process. The original version of the quadruple RF focusing multiple beam system called space lattice focusing (SLF) is supposed for getting intensive ion beam with extremely low divergence. The basic principles of the theoretical approach as well as some possible advances and restrictions for the practical use in RF linac are supposed to be discussed. (5 refs)

Human-machine cooperation for semantic feature listing

Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor. Large language models (LLMs) offer a novel avenue for the automatic generation of such feature lists, but are prone to significant error. Here, we present a new method for combining a learned model of human lexical-semantics from limited data with LLM-generated data to efficiently generate high-quality feature norms.Comment: To be published in the ICLR TinyPaper trac

Immersion in video games, creative self-efficacy, and political participation

Data from a cross-national survey (N = 801) of young adults in Australia, the Philippines, South Korea, and the U.S. (Guam, Hawaii, Continental U.S.) were analyzed to explore the relationships between the three subcomponents of the immersion motivation of video game play—discovery, role-play, and customization (Yee, 2006)—creative self-efficacy, and political participation. Findings reveal role-play and creative self-efficacy are positively associated with political participation; discovery and role-play are positively associated with creative self-efficacy. Furthermore, discovery, role-play, and customization had small indirect effects on political participation via creative self-efficacy

Immersion in video games, creative self-efficacy, and political participation

Data from a cross-national survey (N = 801) of young adults in Australia, the Philippines, South Korea, and the U.S. (Guam, Hawaii, Continental U.S.) were analyzed to explore the relationships between the three subcomponents of the immersion motivation of video game play—discovery, role-play, and customization (Yee, 2006)—creative self-efficacy, and political participation. Findings reveal role-play and creative self-efficacy are positively associated with political participation; discovery and role-play are positively associated with creative self-efficacy. Furthermore, discovery, role-play, and customization had small indirect effects on political participation via creative self-efficacy

Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems

People's associations between colors and concepts influence their ability to interpret the meanings of colors in information visualizations. Previous work has suggested such effects are limited to concepts that have strong, specific associations with colors. However, although a concept may not be strongly associated with any colors, its mapping can be disambiguated in the context of other concepts in an encoding system. We articulate this view in semantic discriminability theory, a general framework for understanding conditions determining when people can infer meaning from perceptual features. Semantic discriminability is the degree to which observers can infer a unique mapping between visual features and concepts. Semantic discriminability theory posits that the capacity for semantic discriminability for a set of concepts is constrained by the difference between the feature-concept association distributions across the concepts in the set. We define formal properties of this theory and test its implications in two experiments. The results show that the capacity to produce semantically discriminable colors for sets of concepts was indeed constrained by the statistical distance between color-concept association distributions (Experiment 1). Moreover, people could interpret meanings of colors in bar graphs insofar as the colors were semantically discriminable, even for concepts previously considered "non-colorable" (Experiment 2). The results suggest that colors are more robust for visual communication than previously thought.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphic

Conceptual structure coheres in human cognition but not in large language models

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. Contemporary large language models (LLMs), however, make it possible to interrogate the latent structure of conceptual representations using experimental methods nearly identical to those commonly used with human participants. The current work utilizes three common techniques borrowed from cognitive psychology to estimate and compare the structure of concepts in humans and a suite of LLMs. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from LLM behavior, while individually fairly consistent with those estimated from human behavior, vary much more depending upon the particular task used to generate responses--across tasks, estimates of conceptual structure from the very same model cohere less with one another than do human structure estimates. These results highlight an important difference between contemporary LLMs and human cognition, with implications for understanding some fundamental limitations of contemporary machine language

Negative emotions boost users activity at BBC Forum

We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users were investigated. Scale free distributions were observed for activity in individual discussion threads as well as for overall activity. The number of unique users in a thread normalized by the thread length decays with thread length, suggesting that thread life is sustained by mutual discussions rather than by independent comments. Automatic sentiment analysis shows that most posts contain negative emotions and the most active users in individual threads express predominantly negative sentiments. It follows that the average emotion of longer threads is more negative and that threads can be sustained by negative comments. An agent based computer simulation model has been used to reproduce several essential characteristics of the analyzed system. The model stresses the role of discussions between users, especially emotionally laden quarrels between supporters of opposite opinions, and represents many observed statistics of the forum.Comment: 29 pages, 6 figure

The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp)

The MESSAGE Integrated Assessment Model (IAM) developed by IIASA has been a central tool of energy-environment-economy systems analysis in the global scientific and policy arena. It played a major role in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC); it provided marker scenarios of the Representative Concentration Pathways (RCPs) and the Shared Socio-Economic Pathways (SSPs); and it underpinned the analysis of the Global Energy Assessment (GEA). Alas, to provide relevant analysis for current and future challenges, numerical models of human and earth systems need to support higher spatial and temporal resolution, facilitate integration of data sources and methodologies across disciplines, and become open and transparent regarding the underlying data, methods, and the scientific workflow. In this manuscript, we present the building blocks of a new framework for an integrated assessment modeling platform; the \ecosystem" comprises: i) an open-source GAMS implementation of the MESSAGE energy++ system model integrated with the MACRO economic model; ii) a Java/database backend for version-controlled data management, iii) interfaces for the scientific programming languages Python & R for efficient input data and results processing workflows; and iv) a web-browser-based user interface for model/scenario management and intuitive \drag-and-drop" visualization of results. The framework aims to facilitate the highest level of openness for scientific analysis, bridging the need for transparency with efficient data processing and powerful numerical solvers. The platform is geared towards easy integration of data sources and models across disciplines, spatial scales and temporal disaggregation levels. All tools apply best-practice in collaborative software development, and comprehensive documentation of all building blocks and scripts is generated directly from the GAMS equations and the Java/Python/R source code