SODAS Data Discussion 14 April 2023
Copenhagen Center for Social Data Science (SODAS) aspirers to be a resource for all students and researchers at the Faculty of Social Sciences. We therefore invite researchers across the faculty to present ongoing research projects, project applications or just a loose idea that relates to the subject of social data science.
The rules are simple: short research presentations of ten minutes are followed by twenty minutes of debate. No papers will be circulated beforehand, and the presentations cannot be longer than five slides.
Presenter: Louis Boucherie, PhD student at the Technical University of Denmark and SODAS affiliate
Title: To be or not to bereal
Abstract: BeReal is an innovative social media application that captures authentic, spontaneous moments in users' daily lives through geolocated photos and selfies. In this presentation, we will provide an overview of how BeReal operates, analyze the rich dataset generated by its user base, and discuss the potential applications of this data in various fields. BeReal works by sending a notification at a random time of the day, requesting users to take a photo of their surroundings and a selfie. These images, accompanied by geolocation data, are then shared with friends and followers on the platform. The idea is that the moments captured are genuine and unfiltered, offering a unique glimpse into users' lives. By analyzing BeReal's data, we can uncover fascinating patterns and trends related to human behavior.
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Presenter: Jonas L. Juul, Carlsberg Fellow postdoc at the Technical University of Denmark and SODAS affiliate
Title: The dynamics of writing style in scientific publishing
Abstract: When two scientists write a paper together, does their joint writing style represent some average of their individual writing styles? Do some scientists impact the future writing style of their collaborators? In what way? Writing style can be quantified using statistical methods from stylometry. One important application of stylometry is author attribution: Determining the most likely author of an anonymous text. Whereas many stylometric methods have been developed to match texts with likely authors, little is known about the writing style in collaborations. Using a corpus of millions of scientific abstracts, their authors and meta data about papers and authors, we seek to quantify the dynamics of writing style in scientific publishing. Ideas for questions, methods and further data is much appreciated.
Joint work in progress with Jon Kleinberg, Cornell University.