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Abstract
Creating a poster with high-resolution network images can be a challenging task. In this article, the process of exporting a network figure from a network analysis tool, importing it in a vector graphics tool, and preparing the poster for print is discussed. The steps include critical strategies for producing print-quality figures that also apply to the preparation of network figures for journal publication. We discuss different file formats and argue that the favorite tool for creating conference posters – Microsoft PowerPoint – is not suitable for posters that include network figures. Instead, we suggest the use of Inkscape, a free and open-source tool, or a comparable vector graphics tool to prepare your posters. The goal of this article is to provide a tutorial-like description of the critical steps for creating a conference poster with high-resolution network figures.

'Simulationen sind Nachbildungen von Abläufen in realen und meist komplexen Systemen. Multiagentensimulationen beschreiben das Verhalten der einzelnen Akteure dieser Systeme. In den Sozialwissenschaften finden Multiagentensimulationen zunehmend Verbreitung (Gilbert & Troitzsch 2005, Gilbert 2007, Miller et al. 2007). Dies ist vor allem durch die fortschreitenden Entwicklungen im Bereich der Homecomputer ermöglicht, da Multiagentensimulationen ohne moderne EDV-Infrastruktur undenkbar wäre. Dieser Artikel gibt im ersten Teil eine Einführung in die Theorie der Modellbildung und Simulation sowie einen Abschnitt zur Simulation als virtuelles Experiment. Der zweite Teil dient der praktischen Einführung in die Simulationsumgebung StarLogo. Simulationsumgebungen werden dabei als inhaltsneutrale, computerbasierte Tools verstanden, in denen komplexe, reale Systeme nachgebildet und simuliert werden können. Ziel des vorliegenden Artikels ist es, Sozialwissenschaftlerinnen und Sozialwissenschaftler ohne Erfahrungen aus dem Bereich der Programmierung in die Programmerstellung von Multiagentensimulationen einzuführen und zur Umsetzung von eigenen Projekten anzuregen und zu motivieren.' (Autorenreferat)

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

As emerging powers forge ahead with big data initiatives, questions arise regarding the implications of these programs for governance in the Global South more broadly. One understudied aspect deals with how actors attribute legitimacy to governments' big data activities. We explore actors' agency in one crucial case: the world's largest demographic and biometric data program, India's Aadhaar. Analyzing roughly 250,000 tweets collected in the first 10 years of Aadhaar's operation, we find that both normative acceptance and cost–benefit calculations are crucial for legitimacy attribution. This finding challenges mainstream theoretical approaches, which prioritize normative factors and often fail to examine how normative and material factors interact during legitimacy attribution. In addition, our study demonstrates a new, mixed-methods approach to measuring legitimacy attribution using Twitter data, which overcomes traditional challenges. As such, we underline the viability of Twitter data as a tool for social measurement.

The digital humanities are considered a research paradigm that led to the entry of computerbased approaches in the humanities. This thesis evaluates the chances and challenges when combining computational methods in historical research and addresses the limitations and implications of its epistemological and ontological changes based on two case studies: on the extend of political judiciary in the Austrian Corporate State, and on the structural analysis of influence networks of intellectuals.

AbstractWhen collecting egocentric network data, visual representations of networks can function as a cognitive aid for depicting relationships, helping to maintain an overview of the relationships, and keeping the attention of the interviewees. Additionally, network maps can serve as a narration generator in qualitative and in mixed-methods studies. While varying visual instruments are used for collecting egocentric network data, little is known about differences among visual tools concerning the influence on the resulting network data, the usability for interviewees, and data validity. The article provides an overview of existing visually oriented tools that are used to collect egocentric networks and discusses their functions, advantages, and limitations. Then, we present results of an experimental study where we compare four different visual tools with regard to networks elicited, manageability, and the impact of follow-up questions. In order to assess the manageability of the four tools, we used the thinking aloud method. The results provide evidence that the decision in favor of a specific visual tool (structured vs. unstructured) can affect the size and composition of the elicited networks. Follow-up questions greatly affect the elicited networks and follow-up cues can level out differences among tools. Respondents tend to prefer the concentric circles tool, with some differences in preferences and manageability of tools between participants with low and those with high socioeconomic status. Finally, assets and drawbacks of the four instruments are discussed with regard to data quality and crucial aspects of the data collection process when using visual tools.

Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.

This dataset consists of IDs of geotagged Twitter posts from within the United States. They are provided as files per day and state as well as per day and county. In addition, files containing the aggregated number of hashtags from these tweets are provided per day and state and per day and county. This data is organized as a ZIP-file per month containing several zip-files per day which hold the txt-files with the ID/hash information.
Also part of the dataset are two shapefiles for the US counties and states and Python scripts for the data collection and sorting geotags into counties.