cs.CLSep 25, 2026
SaveIdentifying Scientists on X
Organizations: Heinrich-Heine-University Düsseldorf, Germany · Heinrich-Heine-University & GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany Düsseldorf, Germany
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.
Figures & tables
Figure 1. Log scaled distribution of tweets per user for scientists for the Scholar corpus
Figure 2. Log scaled distribution of tweets per user for non-scientists for the Scholar corpus
| Data | Tweets of scientists | Scientists | Tweets of non-scientists | Non- scientists |
| Orcid | 21,443 | 578 | 6737 | 508 |
| Scholar | 189,852 | 23,785 | 189,852 | 23,785 |
Table 1. Dataset statistics for Orcid and Scholar datasets.
| Tweet | Biography | Label |
| Election Expert Richard L. Hasen on Voters’ Distrust [URL] | Professor of Law and Political Science at UC Irvine; Election Law Blogger. Coming 2/4/20: Election Meltdown (Preorders [URL]) | Scientist |
| #AcademicTwitter - is there any advantage to editing a special issue of a journal when you otherwise were going to publish your relevant work in other journals of much higher impact factor? | Spatial is my speciality. If you are a coral, your location is a matter of life or death - especially when a cyclone comes! Views my own. #TeamHB3 | Scientist |
| @USER Here’s just one of many articles that debunk these stupid claims. Just Google it. [URL] | Software Engineer. Anything retweeted is not adopted as a statement of fact. | Non-Scientist |
| Anyone used to writing qualitative research papers? Writing this way is just not natural to me and I’m struggling so if anyones familiar pls help me get an A | keepin the fun in dysfunctional | Non-Scientist |
| REPLY: @USER @USER That’s my pick too,,, he would of been so perfect. | Just a very cool Jim Morrison fan …..Cowboys Fan/ addicted to movies & creating art, especially digital :: I Stand with Ukraine & President Biden | Unknown |
Table 2. Example Tweets and user biographies from accounts with the label of the account. Users are anonymized with @USER, Urls with [URL].
| Classifier | Accuracy | Precision | Recall | F1 |
| SVM | 0.84 | 0.85 | 0.82 | 0.84 |
| Random Forest | 0.84 | 0.85 | 0.82 | 0.84 |
| Ada Boost | 0.83 | 0.84 | 0.81 | 0.83 |
| Logistic Reg. | 0.83 | 0.84 | 0.82 | 0.83 |
| SVM + KW | 0.88 | 0.90 | 0.85 | 0.87 |
| Random Forest + KW | 0.88 | 0.89 | 0.86 | 0.88 |
Table 3. Results on a 10-fold cross validation on the Scholar dataset. ’KW’ stands for keyword features (Keyword+Regex), described in Section 4.1 . Recall, Precision and F1 Scores are macro averaged.
| Classifier | Accuracy | Precision | Recall | F1 |
| SVM | 0.77 | 0.81 | 0.74 | 0.77 |
| Random Forest | 0.77 | 0.79 | 0.78 | 0.78 |
| Ada Boost | 0.76 | 0.78 | 0.77 | 0.77 |
| Logistic Reg. | 0.78 | 0.80 | 0.79 | 0.79 |
| SVM + KW | 0.81 | 0.84 | 0.79 | 0.81 |
| Random Forest + KW | 0.80 | 0.83 | 0.79 | 0.81 |
Table 4. Results on a 10-fold cross validation on the Orcid dataset. ’KW’ stands for keyword features (Keyword+Regex), described in Section 4.1 . Recall, Precision and F1 Scores are macro averaged.
| Model | Accuracy | Precision | Recall | F1 |
| Random Forest | 0.84 | 0.85 | 0.82 | 0.84 |
| Random Forest only KW | 0.76 | 0.90 | 0.57 | 0.71 |
| Random Forest + KW | 0.88 | 0.89 | 0.86 | 0.88 |
| DeBERTa Ensemble | 0.94 | 0.94 | 0.95 | 0.94 |
| DeBERTa Tweet | 0.89 | 0.90 | 0.89 | 0.89 |
| DeBERTa Bio | 0.92 | 0.92 | 0.91 | 0.92 |
Table 5. Results of the contrastively fine-tuned models on the 10-fold cross validation on the Scholar dataset. Precision, Recall and F1 are macro averaged. ’KW’ stands for keyword features (Keyword+Regex).
Figure 3. Top 10 most important features for Random Forest for the Orcid dataset and the Scholar dataset with keyword features
Figure 4. Top 10 most important features for Random Forest for the Orcid dataset and the Scholar dataset without keyword features
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
| Fold | Triplet Count Training | Triplet Count Validation | Removed Users with duplicate texts |
| 1 | 34,243 | 8,561 | 9 |
| 2 | 34,242 | 8,561 | 10 |
| 3 | 34,242 | 8,561 | 10 |
| 4 | 34,243 | 8,561 | 9 |
| 5 | 34,247 | 8,562 | 4 |
| 6 | 34,242 | 8,561 | 10 |
Table 6. Triplet summary over 10 folds for Tweet data. Each fold contains 41,653 instances in total. This fold is split into 80% Train and 20% Validation
| Fold | Contrastive Pairs Training | Positive Pairs | Negative Pairs | Validation Pairs | Removed Users with duplicate texts |
| 1 | 34,243 | 17,121 | 17,122 | 8,561 | 9 |
| 2 | 34,242 | 17,121 | 17,121 | 8,561 | 10 |
| 3 | 34,242 | 17,121 | 17,121 | 8,561 | 10 |
| 4 | 34,243 | 17,121 | 17,122 | 8,561 | 9 |
| 5 | 34,247 | 17,123 | 17,124 | 8,561 | 4 |
| 6 | 34,242 | 17,121 | 17,121 | 8,561 | 10 |
Table 7. Contrastive summary over 10 folds for Tweet data. Each fold contains 41,653 instances in total. This fold is split into 80% Train and 20% Validation. The created dataset is balanced,
| Fold | Non-Scientists | Scientists |
| 0 | 21,369 | 21,444 |
| 1 | 21,377 | 21,436 |
| 2 | 21,454 | 21,359 |
| 3 | 21,428 | 21,385 |
| 4 | 21,343 | 21,470 |
| 5 | 21,408 | 21,405 |
Table 8. Statistics for each fold. Folds will be split (not stratified) into training (80%) and validation set (20%).
| Fold | Triplet Count Training | Triplet Count Validation | Removed Users with duplicate texts |
| 1 | 30,891 | 7,723 | 67 |
| 2 | 30,907 | 7,727 | 69 |
| 3 | 30,869 | 7,718 | 62 |
| 4 | 30,871 | 7,718 | 69 |
| 5 | 30,892 | 7,723 | 68 |
| 6 | 30,857 | 7,715 | 76 |
Table 9. Triplet summary over 10 folds for biography . Each fold contains 41,653 instances in total. This fold is split into 80% Train and 20% Validation
| Fold | Contrastive Pairs Training | Positive Pairs | Negative Pairs | Validation Pairs | Removed Users with duplicate texts |
| 1 | 30,891 | 15,445 | 15,446 | 7,723 | 67 |
| 2 | 30,907 | 15,453 | 15,454 | 7,727 | 69 |
| 3 | 30,869 | 15,434 | 15,435 | 7,718 | 62 |
| 4 | 30,871 | 15,435 | 15,436 | 7,718 | 69 |
| 5 | 30,892 | 15,446 | 15,446 | 7,723 | 68 |
| 6 | 30,857 | 15,428 | 15,429 | 7,715 | 76 |
Table 10. Contrastive summary over 10 folds for biography data . Each fold contains 41,653 instances in total. This fold is split into 80% Train and 20% Validation.
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