Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.Keywords cross-media analysis, language models, online campaigning, social media, text analysis Social media have become ubiquitous communication channels for candidates during election campaigns. Platforms like Facebook and Twitter enable candidates to directly reach out to voters, mobilize supporters, and influence the public agenda. These fundamental changes in political communication therefore present election candidates with a widened range of strategic choices. Should candidates address the topics most important to a mass audience? Should they tailor their messages to the specific habits and audiences on social media platforms? Although academic research on social media campaigning has flourished in the past several years (Boulianne, 2016;Jungherr, 2016b), it is still unclear which topics politicians address on these platforms, since previous research mostly concentrated on meta data generated by the use of communication conventions such as retweets, @-mentions, likes, or hashtags. Understanding the ways in which politicians Sebastian Stier, Arnim Bleier, and Haiko Lietz are Postdoctoral Researchers, and Markus Strohmaier is a Scientific Director at GESIS -Leibniz Institute for the Social Sciences.Address correspondence to Dr. Sebastian Stier, Department Computational Social Science, GESIS -Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, Köln, D-50667. E-mail: sebastian.stier@gesis.org Color versions of one or more of the figures in the article can be found online at www. tandfonline.com/UPCP. Political Communication, 35:50-74, 2018© 2017 Taylor & Francis Group, LLC ISSN: 1058 print / 1091-7675 online DOI: https://doi.org/10. 1080/10584609.2017.1334728 adapt the contents of their messages to the peculiarities of different platforms generates deeper insights into how political communication is shaped by social media.Much research revealed a continuation of the status quo in online campaigning, as politicians mostly replicated traditional messages and campaign mo...

Scienti¯c 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 scienti¯c practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-speci¯c di®erences 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 scienti¯c 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.

Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.

Abstract“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.

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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.

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Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.