introduction

1 Introduction

The rise of the Internet, like on many aspects in life, has had an influence on social networks (Licoppe and Smoreda 2005). Because of the internet, selection and influence processes have changed. Literature on mate-selection shows that when using the Internet to find a partner, individuals tend to select partners that show more similarity when compared to couples that selected each other in offline settings (Carter and Buckwalter 2009). Also, within online friendship relations, a high level of homophily is visible (Aiello et al. 2012).

However, less is known about the influence of social media on collaboration networks of scientists. While the collaboration networks of scientists are studied more often (Barabási et al. 2002), the role of social media remains unclear. The current study therefore delves deeper into this role of social media on offline social networks. More specifically, I will not only consider the social media activity, but also its relation to total scientific citations. For this, the Kardashian Index(k-index) (Hall 2014) will be used. This index expresses the ratio between scientists’ popularity on Twitter and their publishing of significant peer-reviewed papers. A high k-index means that a scientist may be ‘overvalued’, while a low k-index means an undervaluation of a scientist.

Investigating the k-index is especially relevant in the context of collaborations of scientists, as it could reveal whether scientists attach more or less value to online “performance” of co-authors than to actual scientific performance. If it is the case that scientists with a high k-index are more popular to be selected for collaboration, this may indicate that there is a shift in focus within academia from scientific production towards importance online (Califf 2020). It is questionable whether this is a desirable development, especially if this will be at the cost of doing good research (Eacott 2020). Furthermore, even if this k-index is not of value, this could indicate that scientists are not critical on co-publishing with over- or undervalued scientists. These insights in possibly altering influence processes for choosing scientific collaborators are also relevant more generally, as it could signal a discrepancy between individuals’ online and offline status, in which online status could become more important.

Following the literature on online mate- and friendship selection (Carter and Buckwalter 2009), it is on the one hand interesting to investigate whether scientists select other scientists that are similarly active on social media. On the other hand, it is possible that scientists with social media popularity will also become attractive to be selected by others to collaborate (Hall, 2014). As no literature yet is able to describe the directions of these influences, in the current paper, I would like to investigate several research questions to gain more insights into the influence of the Kardashian Index on networks of scientists at two academic departments: How do scientists within the Sociology and Data Science department of Radboud University differ in their Kardashian Index? And; How do scientists between the Sociology and Data Science department of Radboud University differ in their Kardashian Index?

While these questions are descriptive in nature, I would also like to investigate network effects. To study the possible influence of the Kardashian Index on a network based on co-publications, I aim to answer the following research question: To what extent does the Kardashian Index influence network dynamics of co-publication networks of the Sociology and Data Science department at Radboud University? The latter question is answered using the Stochastic Actor-Orientated Modelling (SOAM) of RSiena (Ripley et al. 2022), which enables me to investigate the effect of the Kardashian Index on collaboration, while controlling for structural network effects and other characteristics that are of influence on collaboration.

The scientific contribution of this study is thus twofold: First, it adds to the gap in the literature on the influence of social media within collaboration networks of scientists. Second, this is not only done descriptively, but also by performing a SOAM-analysis, taking into account structural network effects. While for instance regression analyses could test which individual characteristics are related to a scientist’s k-index, the current study also looks at whether this k-index influences decisions a scientist makes in their selection of co-authors, and whether this holds when taking into account the structure of the network the scientist is embedded in. This will be investigated by webscraping data on the two departments at Radboud University.

References

Aiello, Luca Maria, Alain Barrat, Rossano Schifanella, Ciro Cattuto, Benjamin Markines, and Filippo Menczer. 2012. “Friendship Prediction and Homophily in Social Media.” ACM Transactions on the Web 6 (2): 9:1–33. https://doi.org/10.1145/2180861.2180866.

Barabási, A. L, H Jeong, Z Néda, E Ravasz, A Schubert, and T Vicsek. 2002. “Evolution of the Social Network of Scientific Collaborations.” Physica A: Statistical Mechanics and Its Applications 311 (3): 590–614. https://doi.org/10.1016/S0378-4371(02)00736-7.

Califf, Robert M. 2020. “A Perspective on the K-Index∗.” JACC: Case Reports 2 (2): 335–36. https://doi.org/10.1016/j.jaccas.2020.01.003.

Carter, Steven R., and J. Galen Buckwalter. 2009. “Enhancing Mate Selection Through the Internet: A Comparison of Relationship Quality Between Marriages Arising from an Online Matchmaking System and Marriages Arising from Unfettered Selection.” Interpersona: An International Journal on Personal Relationships, no. Extra 3: 105–25. https://dialnet.unirioja.es/servlet/articulo?codigo=5216138.

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Hall, Neil. 2014. “The Kardashian Index: A Measure of Discrepant Social Media Profile for Scientists.” Genome Biology 15 (7): 424. https://doi.org/10.1186/s13059-014-0424-0.

Licoppe, Christian, and Zbigniew Smoreda. 2005. “Are Social Networks Technologically Embedded?: How Networks Are Changing Today with Changes in Communication Technology.” Social Networks, The Dynamics of Personal Networks, 27 (4): 317–35. https://doi.org/10.1016/j.socnet.2004.11.001.

Ripley, Ruth M., Tom A. B. Snijders, Zsofia B’oda, Andr’as V"or"os, and Paulina Preciado. 2022. “Manual for Siena Version 4.0.” Oxford: University of Oxford, Department of Statistics; Nuffield College.