Data & Method

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1 Data

In the present study, data will be webscraped from the internet to answer the research question. The study will focus on scientists working at the Sociology department and scientists working at the Data Science department of Radboud University. Information on publications, co-authorship and citations will be scraped from Google Scholar using the Scholar package in R. Social media participation will be expressed using the Kardashian Index, which compares the Twitter followers with number of citations of academics (Hall, 2014). In order to calculate these, the twitteR package in R is used to extract Twitter user information of scholars of the departments. For each full name, the first user scraped is saved. By eye-balling, twitter users with a high amount were checked. In some cases, the match between full name and Twitter username was not made correctly. In those instances, this was corrected by hand. Using the number of followers and the citations scraped from Google Scholar, the Kardashian Index is calculated.

The control variable Twitter Dummy was added to indicate whether the scholar has Twitter or not. Furthermore, the variable Dutch shows the ethnicity of the staff member. Ethnicity was scraped using the Achternamenbank. In case of a typical Dutch last name, the staff member got a score one on the dummy Dutch. In other cases, the scholar scores a zero. This means that all last names that are not Dutch are taken together as one category, not distinguishing between (Non-)Western ethnic background. Similarly to ethnicity, the Meertens namenbank was used to determine the gender of the respondent. In case of a first name that is more often used for women than for men, a scholar scores a zero on the dummy gender. In case of a first name that is more often used for men, the scholar scores a one on the dummy.

Data will be collected for two different times, and then the evolution of the networks of Sociology and Data Science will be compared.

Summarized, it can be expressed as follows:

Nodes The scientists of both departments mentioned (thus focusing on the micro-level)

Edges Co-authorship between these scientists (yes/no)

Dependent variable Not exactly sure yet. Density of the network. Homophily? But I don’t have everyone’s research interests.

Independent variable Kardashian Index of the scientists. To measure the influence effects, a model will be analyzed including the Kardashian Index as a dependent variable.

Control variables Gender and age.

2 Method

In order to study the networks, the RSiena package will be used. SIENA is able to analyze longitudinal data simulating network outcomes based on the perspective of the nodes (Ripley, Snijders, Boda, Voros & Preciado, 2022) TO WHAT EXTENT DO I EXPLAIN THIS? First, the evaluation effects will be studied, which focuses on which ties are present.

3 References

Hall, N. (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

Ruth M. Ripley, Tom A.B. Snijders, Zs ́ofia Boda, Andr ́as V ̈or ̈os, and Paulina Preciado, 2022. Manual for SIENA version 4.0 (version August 11, 2022). Oxford: University of Oxford, Department of Statistics; Nuffield College.