Classifying scientists and non-scientists on X with language features
Identifying Scientists on X
Computation and Language
Summary
It can be hard to tell who is a scientist on social media just by looking at their profile. The authors developed a way to automatically identify scientists on X (formerly Twitter) by analyzing their biographies and tweets. They tested two methods, one using simpler language features and another using a more advanced AI model, achieving high accuracy. They also shared datasets of users labeled as scientists or not to help others with similar tasks.
What this means in practice
- •For social media platform teams: Automatically identify scientist accounts to improve content curation and targeted features for science-related discourse on X.
- •For science communication organizations: Find scientist users on X to better target outreach programs and monitor science discussions online.
Authors
Philipp Meier, Katarina Boland, Laura Kallmeyer, Stefan Dietze
Abstract
With the growing importance of science-related discourse on the Web and the erosion of the classical knowledge order, it is important to identify different user groups, such as scientists, automatically. This work proposes an approach for identifying scientists and non- scientists on X/Twitter based on their user biographies and tweets. We show that we are able to classify accounts as scientists and non- scientists on two different datasets, reaching an F1 score of up to 0.88 using Random Forests with linguistic features and up to 0.96 using a contrastively fine-tuned DeBERTa model in an ensemble setup. Furthermore, we provide two datasets with X users labeled as scientists or non scientists and their respective tweets and user biographies.