Suchergebnisse

This book offers a framework for the analysis of political communication in election campaigns based on digital trace data that documents political behavior, interests and opinions. The author investigates the data-generating processes leading users to interact with digital services in politically relevant contexts. These interactions produce digital traces, which in turn can be analyzed to draw inferences on political events or the phenomena that give rise to them. Various factors mediate the image of political reality emerging from digital trace data, such as the users of digital services' political interests, attitudes, or attention to politics. In order to arrive at valid inferences about the political reality on the basis of digital trace data, these mediating factors have to be accounted for. The author presents this interpretative framework in a detailed analysis of Twitter messages referring to politics in the context of the 2009 federal elections in Germany. This book will appeal to scholars interested in the field of political communication, as well as practitioners active in the political arena

This book offers a framework for the analysis of political communication in election campaigns based on digital trace data that documents political behavior, interests and opinions. The author investigates the data-generating processes leading users to interact with digital services in politically relevant contexts. These interactions produce digital traces, which in turn can be analyzed to draw inferences on political events or the phenomena that give rise to them. Various factors mediate the image of political reality emerging from digital trace data, such as the users of digital services' political interests, attitudes, or attention to politics. In order to arrive at valid inferences about the political reality on the basis of digital trace data, these mediating factors have to be accounted for. The author presents this interpretative framework in a detailed analysis of Twitter messages referring to politics in the context of the 2009 federal elections in Germany. This book will appeal to scholars interested in the field of political communication, as well as practitioners active in the political arena.

Digital trace data are an important resource for the study of social life and human behavior. Digital trace data document users' interactions with digital information systems. This includes interactions on social media services – like Facebook, TikTok, or X (Twitter) – as well as interactions with devices – such as smart phones or smart speakers. This makes digital trace data into an important source for the analysis of communicative phenomena in digital media and beyond. But to realize their potential, researchers must first account for their characteristics as measures of society and behavior. This includes questions as to what phenomena they do speak directly, which can be inferred based on theorizing the links between specific data generating processes and phenomena of interest, what remains hidden, and how related uncertainty can be accounted for or even quantified.

The success and widespread deployment of artificial intelligence (AI) have raised awareness of the technology's economic, social, and political consequences. Each new step in the development and application of AI is accompanied by speculations about a supposedly imminent but largely fictional artificial general intelligence (AGI) with (super-)human capacities, as seen in the unfolding discourse about capabilities and impact of large language models (LLMs) in the wake of ChatGPT. These far-reaching expectations lead to a discussion on the societal and political impact of AI that is largely dominated by unfocused fears and enthusiasms. In contrast, this article provides a framework for a more focused and productive analysis and discussion of AI's likely impact on one specific social field: democracy. First, it is necessary to be clear about the workings of AI. This means differentiating between what is at present a largely imaginary AGI and narrow artificial intelligence focused on solving specific tasks. This distinction allows for a critical discussion of how AI affects different aspects of democracy, including its effects on the conditions of self-rule and people's opportunities to exercise it, equality, the institution of elections, and competition between democratic and autocratic systems of government. This article shows that the consequences of today's AI are more specific for democracy than broad speculation about AGI capabilities implies. Focusing on these specific aspects will account for actual threats and opportunities and thus allow for better monitoring of AI's impact on democracy in an interdisciplinary effort by computer and social scientists.

Large language models (LLMs), like ChatGPT, GitHub Copilot, and Microsoft Copilot, present challenges in university education, particularly for paper assignments. These AI-driven tools enable students to (semi)automatically complete tasks that were previously considered evidence of skill acquisition, potentially affecting grading and skill development. However, the use of these tools is not legally considered plagiarism and is becoming increasingly integrated into various software solutions.

University education in the social sciences aims to develop students' abilities to make sense of the world, connect their observations with abstract structures, measure phenomena of interest, systematically test expectations, and present findings in structured accounts. These practices are learned through repeated performance of tasks, such as writing research papers. LLM applications like ChatGPT create conflicting incentives for students, who might rely on them to produce parts of their papers instead of engaging in the learning process.

While LLMs can be helpful tools for knowledge discovery, writing assistance, and coding assistance, using them effectively and safely requires an understanding of their underlying mechanisms, potential weaknesses, and enough domain knowledge to identify mistakes. This makes LLMs particularly challenging for students in the early stages of acquiring scientific skills and domain knowledge.

Educators must enable and train students to responsibly use these new tools, reflecting on the underlying tensions and their strengths and weaknesses for academic writing tasks. This working paper aims to provide guidelines on responsible LLM use in academic contexts, specifically for students at the Chair for the Governance of Complex and Innovative Technological Systems at the University of Bamberg. The paper discusses the function of written paper assignments, the tasks necessary to complete them, and evaluates ChatGPT's performance in assisting with these tasks. It concludes with observations and advice for students to maximize the benefits of LLMs while mitigating potential risks in academic contexts, focusing on enabling learning.

Digital media have become important elements of political parties and campaign organizations all over the world. Examples from U.S. presidential campaigns dominate the public imagination of digital campaigning. At the same time, it is important to recognize that the uses of digital media in politics vary considerably depending on campaign contexts and resources. This makes necessary the analysis of the use of digital media by parties and campaign organizations in varying temporal and international contexts instead of expecting the examples from the U.S. to apply globally. The chapter will proceed by a discussion of some of the dominant theoretical and empirical approaches to the study of digital campaigning. Following this, the chapter will use a framework focused on campaign functions for the discussion of digital campaigning: The impact of digital media on organizational structures and work routines, resource collection and allocation, achieving presence in communication spaces and reach to audiences of interest, and the use of digital media as a symbol for professionalism and innovation of parties and their candidates.

This article presents a case study of the use of digital tools by campaign organizations in Germany's 2013 federal election. Based on observations and in-depth interviews with key personnel in the campaigns of six of the parties running for Parliament, I examine whether German campaigns' use of digital tools follows the usage practices that have been identified in studies of campaigns in the United States. I group how campaigns use digital tools into four categories: organizational structures and work routines, presence in information spaces online, support in resource collection and allocation, and symbolic uses. I show that these categories capture how German parties use digital tools. U.S.-based studies can thus provide helpful interpretive frameworks for studying digital campaigning in other countries. However, I also reveal that there are important differences between German and U.S.-based online campaigning. These differences stem from the different levels of intensity with which digital tools are deployed in each country.