Identifying Scientists on X
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Computer Science > Computation and Language
Title:Identifying Scientists on X
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.
| Comments: | Corrected version of Identifying Scientists on X published at Companion Publication of the 18th ACM Web Science Conference 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.31264 [cs.CL] |
| (or arXiv:2609.31264v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.31264
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Companion Publication of the 18th ACM Web Science Conference (2026) 155-164 |
| Related DOI: | https://doi.org/10.1145/3795513.3810448
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