Suchergebnisse

"Big" digital behavioral data increasingly allows large-scale and high-resolution analyses of the behavior and performance of persons or aggregated identities in whole fields. Often the desired system of study is only a subset of a larger database. The task of drawing a field boundary is complicated because socio-cultural systems are highly overlapping. Here, I propose a sociologically enhanced information retrieval method to delineate fields that is based on the reproductive mechanism of fields, able to account for field heterogeneity, and generally applicable also outside scientometric, e.g., in social media, contexts. The method is demonstrated in a delineation of the multidisciplinary and very heterogeneous Social Network Science field using the Web of Science database. The field consists of 25,760 publications and has a historical dimension (1916-2012). This set has high face validity and exhibits expected statistical properties like systemic growth and power law size distributions. Data is clean and disambiguated. The dataset with 45,580 author names and 23,026 linguistic concepts is publically available and supposed to enable high-quality analyses of an evolving complex socio-cultural system.

"Was ist Emerging Science, und wie lässt sich messen, ob es sich bei einem Fachgebiet um Emerging Science handelt oder nicht? Häufig wird Emerging Science als Forschungsfront mit Hilfe verschiedener Verfahren der Zitationsanalyse diagnostiziert. Bettencourt et al. verwenden Kollaborationsanalysen und legen das Augenmerk auf eine strukturelle Diagnose des Emergenzprozesses an sich. Dem Modell nach zeigt sich die Etablierung eines Paradigmas in einem Fachgebiet in Form einer topologischen Veränderung seiner Sozialstruktur. In der vorliegenden Arbeit wird das Modell auf die 'New Science of Networks' und das Fachgebiet der Szientometrie angewendet. Erwartungsgemäß zeigen sich Unterschiede in ihren Evolutionsprozessen. Modell und Methoden der Netzwerkanalyse werden vor dem Hintergrund der Wissenschaftsforschung, der Komplexitätstheorie und der Relationalen Soziologie diskutiert. Besondere Berücksichtigung findet dabei die Selbstähnlichkeit des Wissenschaftssystems. Diskutiert werden auch die Auswirkungen unterschiedlicher Zählmethoden auf die Ergebnisse der Untersuchungen." (Autorenreferat)

Die Nutzung von digitalen Technologien, wie Social-Media-Plattformen, hinterlässt riesige Mengen an Verhaltensspuren, die für die Sozialforschung überaus interessant sind. Andere digitale Technologien, wie Mobiltelefone, ermöglichen die Erhebung von Verhaltensspuren zu Forschungszwecken. Diese digitalen Verhaltensdaten bestehen aus genuin relationalen Beziehungen, die als Netzwerke betrachtet werden können. Diese Art von Daten erfordert eine Verlagerung der Perspektive von Individuen zu Mikroereignissen (z.B. ein Post in den sozialen Medien) als Beobachtungseinheiten und stellt etablierte Techniken, wie die Analyse sozialer Netzwerke, in den Mittelpunkt. Wir argumentieren, dass die Verwendung dieses Ansatzes, die Ermittlung individueller Eigenschaften und Einstellungen sowie die Offenlegung von Mikro-Makro-Verhaltensdynamiken mittels Auswertung von Mustern potenziell nützliche Anwendungen sind. Wir diskutieren methodische Herausforderungen und gelangen zu der Schlussfolgerung, dass soziale Theorie ein Grundpfeiler für die Konsolidierung der Computational Social Science (computergestützte Sozialwissenschaft) ist.

Our uses of digital technologies like social media platforms or email leave massive amounts of behavioral traces that are most interesting for social research. Other digital technologies like cell phones allow harnessing behavioral traces for research purposes. Such Digital Behavioral Data consists of genuinely relational records which can be thought of in terms of networks. However, this kind of data requires a shift of perspective from individuals to micro events (e.g., a post on social media) as units of observation and brings established techniques like Social Network Analysis to the center stage. We argue that, using this approach, obtaining individual attributes and attitudes as well as uncovering the micro-macro dynamics of behavior by mining patterns are potentially fruitful applications. We discuss methodological challenges and conclude that social theory is a constitutive pillar for the consolidation of Computational Social Science.

DBLP (https://dblp.org/) is a comprehensive collection of computer science publications from major and minor journals and conference proceedings. From this dump, we remove arXiv preprints. Our dataset consists of 1.9 million publications from 1970 to 2014 that are authored by 1.1 million authors. We have added citations among publications by combining DBLP with the AMiner dataset (https://www.aminer.org/citation) via publication titles and years. There are 6.6 million citations among publications. Author names in DBLP are disambiguated. To infer the gender of authors, we have used a method that combines the results of name-based and image-based gender detection services. Since the accuracy is very low for Chinese and Korean names, we label their gender as unknown to reduce noise in our analysis.

Social Network Science (SNS) is the field concerned with studying social systems in a relational way from the perspectives of the social and natural sciences. This data set consists of 25,760 biographical records retrieved from the Web of Science, ranging from 1916 to 2012. Each publication belongs to one of five subfields. To facilitate analyses of the social aspect of SNS, the names of 45,580 distinct authors are provided, linked to the papers in 68,227 publication-author relations. Author names have been disambiguated semi-automatically. To enable analyses of the cultural aspect of SNS, 23,026 distinct linguistic concepts are provided. These concepts resemble words or word combinations extracted from titles (for all publication years) and from abstracts and author keywords (only for publications published in, or after, 1990). They are linked to the papers in 201,608 publication-concept relations.

A newer version of this dataset is available at https://doi.org/10.7802/1.1954

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Social Network Science (SNS) is the field concerned with studying social systems in a relational way from the perspectives of the social and natural sciences. This data set consists of 25,760 biographical records retrieved from the Web of Science, ranging from 1916 to 2012. Each publication belongs to one of five subfields. To facilitate analyses of the social aspect of SNS, the names of 45,580 distinct authors are provided, linked to the papers in 68,227 publication-author relations. Author names have been disambiguated semi-automatically. To enable analyses of the cultural aspect of SNS, 23,026 distinct linguistic concepts are provided. These concepts resemble words or word combinations extracted from titles (for all publication years) and from abstracts and author keywords (only for publications published after 1990/1991). They are linked to the papers in 202,181 publication-concept relations.

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adopt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages.

"Assessing political conversations in social media requires a deeper understanding of the underlying practices and styles that drive these conversations. In this paper, we present a computational approach for assessing online conversational practices of political parties. Following a deductive approach, we devise a number of quantitative measures from a discussion of theoretical constructs in sociological theory. The resulting measures make different - mostly qualitative - aspects of online conversational practices amenable to computation. We evaluate our computational approach by applying it in a case study. In particular, we study online conversational practices of German politicians on Twitter during the German federal election 2013. We find that political parties share some interesting patterns of behavior, but also exhibit some unique and interesting idiosyncrasies. Our work sheds light on (i) how complex cultural phenomena such as online conversational practices are amenable to quantification and (ii) the way social media such as Twitter are utilized by political parties." (author's abstract)

We introduce two novel ways to capture the impact benefits two countries receive from collaborations.
For both indicators we compare the value of a specific collaboration with the value of average collaborations for each of the countries. As we restrict our analysis to only two-country collaborations and calculate the values for each scientific field individually, many of the problems
introduced by former attempts of collaboration indicators dissipate. Additional to a mean based indicator (Citation Benefit) we introduce an International Citation Share indicator that
measures the share of international citations on an item basis. By aggregating and correlating
these indicators we show that two different factors of collaboration return, highly cited publications and a general domestic/international bias, i.e. the tendency of a publication to be cited more in the originating country, can be measured exclusively by these two indicators. These
approaches open up the field to a new kind of deep analysis of scientific collaborations. (author's abstract)