The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments

An audience's prior beliefs and morals are strong indicators of how likely they will be affected by a given argument. Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement. In argumentation technology, however, this is barely exploited so far. This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences. Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals. In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments. Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments

Analyzing the Use of Metaphors in News Editorials for Political Framing

Metaphorical language is a pivotal element in the realm of political framing. Existing work from linguistics and the social sciences provides compelling evidence regarding the distinctiveness of conceptual framing for political ideology perspectives. However, the nature and utilization of metaphors and the effect on audiences of different political ideologies within political discourses are hardly explored. To enable research in this direction, in this work we create a dataset, originally based on news editorials and labeled with their persuasive effects on liberals and conservatives and extend it with annotations pertaining to metaphorical usage of language. To that end, first, we identify all single metaphors and composite metaphors. Secondly, we provide annotations of the source and target domains for each metaphor. As a result, our corpus consists of 300 news editorials annotated with spans of texts containing metaphors and the corresponding domains of which these metaphors draw from. Our analysis shows that liberal readers are affected by metaphors, whereas conservatives are resistant to them. Both ideologies are affected differently based on the metaphor source and target category. For example, liberals are affected by metaphors in the Darkness & Light (e.g., death) source domains, where as the source domain of Nature affects conservatives more significantly

Analyzing the Persuasive Effect of Style in News Editorial Argumentation

News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies. Previous research has investigated such persuasive effects for argumentative content. In contrast, this paper studies how important the style of news editorials is to achieve persuasion. To this end, we first compare content- and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations. We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content. Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation

Quantifying Synergy between Software Projects using README Files Only

Software version control platforms, such as GitHub, host millions of open-source software projects. Due to their diversity, these projects are an appealing realm for discovering software trends. In our work, we seek to quantify synergy between software projects by connecting them via their similar as well as different software features. Our approach is based on the Literature-Based-Discovery (LBD), originally developed to uncover implicit knowledge in scientific literature databases by linking them through transitive connections. We tested our approach by conducting experiments on 13,264 GitHub (open-source) Python projects. Evaluation, based on human ratings of a subset of 90 project pairs, shows that our developed models are capable of identifying potential synergy between software projects by solely relying on their short descriptions (i.e. readme files)

Persuasiveness of News Editorials depending on Ideology and Personality

News editorials aim to shape the opinions of their readership and the general public on timely controversial issues. The impact of an editorial on the reader’s opinion does not only depend on its content and style, but also on the reader’s profile. Previous work has studied the effect of editorial style depending on general political ideologies (liberals vs.conservatives). In our work, we dig deeper into the persuasiveness of both content and style, exploring the role of the intensity of an ideology (lean vs.extreme) and the reader’s personality traits (agreeableness, conscientiousness, extraversion, neuroticism, and openness). Concretely, we train content- and style-based models on New York Times editorials for different ideology- and personality-specific groups. Our results suggest that particularly readers with extreme ideology and non ‘‘role model” personalities are impacted by style. We further analyze the importance of various text features with respect to the editorials’ impact, the readers’ profile, and the editorials’ geographical scope

Corpus Annotation Graph Builder (CAG): An Architectural Framework to Create and Annotate a Multi-source Graph

Graphs are a natural representation of complex data as their structure allows users to intuitively discover (often implicit) relations among the nodes. Applications build graphs in an ad-hoc fashion, usually tailored to specific use cases, limiting their reusability. To account for this, we present the Corpus Annotation Graph (CAG) architectural framework based on a create-and-annotate pattern that enables users to build uniformly structured graphs from diverse data sources and extend them with automatically extracted annotations (e.g., named entities, topics). The resulting graphs can be used for further analyses across multiple downstream tasks

From causes to consequences, from chat to crisis. The different climate changes of science and Wikipedia

Understanding how society reacts to climate change means understanding how different societal subsystems approach the challenge. With the help of a heuristic of systems theory two subsystems of society – science and mass media – are compared with respect to communications about climate change over the last 20 years. With text mining methods metadata of documents from two databases – OpenAlex and Wikipedia – are generated, analyzed, and visualized. We find substantial differences as well as similarities in the social, factual, and temporal dimensions. While Wikipedia shows a much greater variety of concrete organizations, social movements, media outlets, and persons, science is more concerned with abstract interrelations of human action. In both systems, there is a shift in attention from describing the very phenomenon to questioning how to deal with this fact. This demonstrates for science a discursive shift from causes to consequences and for mass media a shift from chat to crisis. Science shows an ongoing growth process, while the attention of mass media appears cyclical