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This article describes patterns of scientific growth that emerge in response to major research accomplishments in instrumentation and the discovery of new matter. Using two Nobel Prize‐winning contributions, the scanning tunneling microscope (STM) and the discovery of Buckminsterfullerenes (BUF), we examine the growth of follow‐up research via citation networks at the author and subdiscipline level. A longitudinal network analysis suggests that structure, cohesiveness, and interdisciplinarity vary considerably with the type of breakthrough and over time. Scientific progress appears to be multifaceted, including not only theoretical advances but also the discovery of new instrumentation and new matter. In addition, we argue that scientific growth does not necessarily lead to the formation of new specialties or new subdisciplines. Rather, we observe the emergence of a research community formed at the intersection of subdisciplinary boundaries.

We map the topic structure of psychology utilizing a sample of over 500,000 abstracts of research articles and conference proceedings spanning two decades (1995–2015). To do so, we apply structural topic models to examine three research questions: (i) What are the discipline’s most prevalent research topics? (ii) How did the scientific discourse in psychology change over the last decades, especially since the advent of neurosciences? (iii) And was this change carried by high impact (HI) or less prestigious journals? Our results reveal that topics related to natural sciences are trending, while their ’counterparts’ leaning to humanities are declining in popularity. Those trends are even more pronounced in the leading outlets of the field. Furthermore, our findings indicate a continued interest in methodological topics accompanied by the ascent of neurosciences and related methods and technologies (e.g. fMRI’s). At the same time, other established approaches (e.g. psychoanalysis) become less popular and indicate a relative decline of topics related to the social sciences and the humanities.

Today's world allows people to connect over larger distances and in shorter intervals than ever before, widely monitored by massive online data sources. Ongoing worldwide computerization has led to completely new opportunities for social scientists to conceive human interactions and relations in unknown precision and quantities. However, the large data sets require techniques that are more likely to be found in computer and natural sciences than in the established fields of social relations. In order to facilitate the participation of social scientists in an emerging interdisciplinary research branch of "computational social science," we propose in this article the usage of the Python programming language. First, we carve out its capacity to handle "Big Data" in suitable formats. Second, we introduce programming libraries to analyze large networks and big text corpora, conduct simulations, and compare their performance to their counterparts in the R environment. Furthermore, we highlight practical tools implemented in Python for operational tasks like preparing presentations. Finally, we discuss how the process of writing code may help to exemplify theoretical concepts and could lead to empirical applications that gain a better understanding of the social processes initiated by the truly global connections of the Internet era.

Most studies concerned with empirical social networks are conducted on the level of individuals. The interaction of scientists is an especially popular research area, with the growing importance of international collaboration as a common sense result. To analyze patterns of cooperation across nations, this paper investigates the structure and evolution of cross-country co-authorships for the field of economics from 1985 to 2011. For a long time economic research has been strongly US centered, while influencing real-world politics all over the globe. We investigate the impact of the general trend of increasing international collaboration on the hegemonic structures in the "global department of economics." A dynamic map of economic research is derived and reveals communities that are hierarchical and structured along the lines of external social forces, i.e. historical and political dimensions. Based on these findings, we discuss the influence of the core-periphery structure on the production of economic knowledge and the dissemination of new ideas.

Is the pursuit of interdisciplinary or innovative research beneficial or detrimental for the impact of early career researchers? We focus on young scholars as they represent an understudied population who have yet to secure a place within academia. Which effects promise higher scientific recognition (i.e., citations) is therefore crucial for the high-stakes decisions young researchers face. To capture these effects, we introduce measurements for interdisciplinarity and novelty that can be applied to a researcher’s career. In contrast to previous studies investigating research impact on the paper level, hence, our paper focuses on a career perspective (i.e., the level of authors). To consider different disciplinary cultures, we utilize a comprehensive dataset on U.S. physicists (n = 4003) and psychologists (n = 4097), who graduated between 2008 and 2012, and traced their publication records. Our results indicate that conducting interdisciplinary research as an early career researcher in physics is beneficial, while it is negatively associated with research impact in psychology. In both fields, physics and psychology, early career researchers focusing on novel combinations of existing knowledge are associated with higher future impact. Taking some risks by deviating to a certain degree from mainstream paradigms seems therefore like a rewarding strategy for young scholars.

The recent growth of alternative media sites and sources has also seen the rise of an aggressive rhetoric decrying mass media or parts thereof as being untrustworthy and politically biased. While it is unclear whether the fake news debate is directly connected with this, it is surely a framing of mass media. In this article, we use techniques of quantitative text analysis in order to analyse how the fake news frame is structured and to understand its central determinants in terms of social context and political orientation. Using quantitative text analysis, we analyse the frame usage and semantic embeddedness in eight blogs. We find evidence for a generalised frame that tends to be independent of political orientation of the blog.