Learning Universal Authorship Representations
Open accessAbstract
Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
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Topic diversity and dataset size are crucial for effective cross-domain transfer.
“Rivera-Soto et al. [2021] concluded that topic diversity and dataset size are crucial for effective cross-domain transfer.”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Rivera-Soto et al., 2021
The Conclusion attributes Reddit’s strong cross-domain transfer to both topic diversity and dataset size. But §4.2 says size only partially explains Reddit’s performance and that increasing the number of Amazon authors yielded marginal gains. Calling both factors “crucial” for cross-domain transfer generally is stronger than the paper’s finding. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAuthorship attribution methods are typically divided into classification-based methods for a small set of candidate authors and similarity-based ranking methods for larger numbers of authors.
“Authorship attribution methods are typically divided into classification-based methods for a small set of candidate authors and similarity-based ranking methods for larger numbers of authors [Rivera-Soto et al., 2021;Huertas-Tato et al., 2022].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Rivera-Soto et al., 2021
The Introduction (page unavailable) contrasts a prior closed-world study of 21 authors with this paper’s open-world setting involving far more authors. The Experiments metrics passage (page unavailable) describes ranking candidates by cosine similarity. The paper does not say that authorship attribution methods are typically divided into classification-based and similarity-based categories, or that classification is generally used for small candidate sets. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn human authorship attribution, where there are a large number of candidate authors, retrieval metrics such as Mean Reciprocal Rank (MRR) and recall-at-k are used.
“In human authorship attribution, where there are a large number of candidate authors, retrieval metrics such as Mean Reciprocal Rank (MRR) and recall-at-k are used [Rivera-Soto et al., 2021].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Rivera-Soto et al., 2021
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Cites
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BackgroundSentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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MethodDomain-Adversarial Training of Neural Networks
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“In future work, it would be interesting to attempt to explicitly disentangle the learned representations into content and style, for example, by using domain adversarial training objectives (Ganin et al., 2016; Bischoff et al., 2020).”
BackgroundSupervised Contrastive Learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., & Krishnan, D. (2020) · arXiv · doi:10.48550/arxiv.2004.11362 · Cited by 3,569 · cited 3× · see citing works
“Additional data augmentation methods, particularly if applied together during training and incorporated into the loss calculation via multiple independent views of the data (Khosla et al., 2020), have the potential to further improve verification performance.”
MethodNeighbourhood Components Analysis
Goldberger, J., Hinton, G. E., Roweis, S. T., & Salakhutdinov, R. (2004) · Cited by 1,855 · see citing works
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
Ni, J., Li, J., & McAuley, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1018 · Cited by 1,283 · cited 1× · see citing works
“We restrict the corpus of Amazon product reviews described in Ni et al. (2019) to the reviews composed by the 135K authors contributing at least 100 reviews each.”
MethodA survey of modern authorship attribution methods
Stamatatos, E. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21001 · Cited by 1,176 · cited 1× · see citing works
“Authorship verification techniques have traditionally relied on modeling stylometric linguistic properties, such as punctuation and whitespace usage and the frequencies of function words, parts-of-speech, and character n -grams (Stamatatos, 2009; Stolerman et al., 2014).”
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Kudo, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1007 · Cited by 1,021 · cited 1× · see citing works
“At training time our model randomly samples excerpts consisting of N = 32 consecutive byte-pair encoded tokens (Kudo, 2018) from each of w contiguous documents, where w varies from 1 to 16 by taking w = (cid:100) 1 + 15 x (cid:101) , where x ∼ Beta (3 , 1) .”
MethodBaumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7347 · Cited by 881 · see citing works
Underspecification Presents Challenges for Credibility in Modern Machine Learning
D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.03395 · Cited by 398 · cited 1× · see citing works
“This may be attributable in part to un-derspecification : the expressivity of the neural network results in fitting domain-specific predictive patterns instead of generalizable features of author-ship (D’Amour et al., 2020).”
BackgroundConvolutional Neural Networks for Authorship Attribution of Short Texts
Shrestha, P., Sierra, S., Gonzalez, F. A., Montes, M., Rosso, P., & Solorio, T. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2106 · Cited by 172 · cited 1× · see citing works
“More recently, end-to-end models built with convolutional neural networks (Shrestha et al., 2017; Andrews and Bishop, 2019) and recurrent neural networks (Boenninghoff et al., 2019) have been proposed.”
BackgroundExplainable Authorship Verification in Social Media via Attention-based Similarity Learning
Boenninghoff, B., Hessler, S., Kolossa, D., & Nickel, R. M. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005650 · Cited by 66 · cited 1× · see citing works
“More recently, end-to-end models built with convolutional neural networks (Shrestha et al., 2017; Andrews and Bishop, 2019) and recurrent neural networks (Boenninghoff et al., 2019) have been proposed.”
BackgroundBertAA : BERT fine-tuning for Authorship Attribution.
Fabien, M., Villatoro-Tello, E., Motlíček, P., & Parida, S. (2020) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 64 · see citing works
Cross-Domain Authorship Attribution Using Pre-trained Language Models
Barlas, G., & Stamatatos, E. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49161-1_22 · Cited by 54 · cited 2× · see citing works
“Prior work has explored self-attention models for author-ship attribution (Saedi and Dras, 2020; Fabien et al., 2020; Barlas and Stamatatos, 2020) with mixed success compared to simpler convolutional models.”
BackgroundMethodMasking topic‐related information to enhance authorship attribution
Stamatatos, E. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23968 · Cited by 51 · cited 1× · see citing works
“Inspired by Stamatatos (2018) we also experimented with using word frequency to identify and mask only topic words, but found it difficult to identify suitable word frequency thresholds.”
MethodBevendorff, J., Ghanem, B., Giachanou, A., Kestemont, M., Manjavacas, E., Markov, I., Mayerl, M., Potthast, M., Rangel, F., Rosso, P., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58219-7_25 · Cited by 37 · see citing works
Siamese networks for large-scale author identification
Saedi, C., & Dras, M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101241 · Cited by 30 · cited 1× · see citing works
“Prior work has explored self-attention models for author-ship attribution (Saedi and Dras, 2020; Fabien et al., 2020; Barlas and Stamatatos, 2020) with mixed success compared to simpler convolutional models.”
MethodBreaking the Closed-World Assumption in Stylometric Authorship Attribution
Stolerman, A., Overdorf, R., Afroz, S., & Greenstadt, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-44952-3_13 · Cited by 28 · cited 1× · see citing works
“Authorship verification techniques have traditionally relied on modeling stylometric linguistic properties, such as punctuation and whitespace usage and the frequencies of function words, parts-of-speech, and character n -grams (Stamatatos, 2009; Stolerman et al., 2014).”
The Importance of Suppressing Domain Style in Authorship AnalysisPreprint
Bischoff, S., Deckers, N., Schliebs, M., Thies, B., Hagen, M., Stamatatos, E., Stein, B., & Potthast, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.14714 · Cited by 25 · cited 1× · see citing works
“In future work, it would be interesting to attempt to explicitly disentangle the learned representations into content and style, for example, by using domain adversarial training objectives (Ganin et al., 2016; Bischoff et al., 2020).”
BackgroundLearning Invariant Representations of Social Media Users
Andrews, N., & Bishop, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1178 · Cited by 19 · cited 5× · see citing works
“For domains that supply timestamps, we stipulate that the held-out evaluation data must be future to the training data in order to assess robustness to ephemeral aspects of writing, such as topic (Andrews and Bishop, 2019).”
BackgroundMethodInfluentialSelf-citationDeep Bayes Factor Scoring for Authorship VerificationPreprint
Boenninghoff, B., Rupp, J., Nickel, R. M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10105 · Cited by 17 · cited 1× · see citing works
“Previous studies have shown that randomly sampling windows improves model generalizability and provides a simple way to handle very long documents (Boenninghoff et al., 2020), while Khan et al. (2021) show that sampling the number w of documents improves a model’s ability to handle collections of…”
BackgroundA Deep Metric Learning Approach to Account Linking
Khan, A., Fleming, E., Schofield, N., Bishop, M., & Andrews, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.415 · Cited by 14 · cited 4× · see citing works
“In particular, the in-domain Reddit results are better than those reported in Khan et al. (2021), establishing a new state-of-the-art.”
BackgroundResultInfluentialSelf-citation
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A taxonomy and review of generalization research in NLPPreprint
Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000638992 · Cited by 41 · see citing works
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
Huang, B., Chen, C., & Shu, K. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715076 · Cited by 42 · see citing works
“These approaches, while offering higher performance, often sacrifice explainability for accuracy (Rivera-Soto et al., 2021).”
InfluentialTDRLM: Stylometric learning for authorship verification by Topic-Debiasing
Hu, X., Ou, W., Acharya, S., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120745 · Cited by 17 · see citing works
Understanding writing style in social media with a supervised contrastively pre-trained transformer
Huertas‐Tato, J., Martín, A. G., & Camacho, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111867 · Cited by 15 · see citing works
CaML: Carbon Footprinting of Household Products with Zero-Shot Semantic Text Similarity
Balaji, B., Vunnava, V. S. G., Guest, G., & Kramer, J. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583882 · Cited by 13 · see citing works
“SBERT has been used in a similar fashion for applications such as fact-checking [24], cyberbullying detection [15], and author representation [36].”
MethodEvaluating Synthetic Data Generation from User Generated Text
Chim, J., Ive, J., & Liakata, M. (2024) · Computational Linguistics · doi:10.1162/coli_a_00540 · Cited by 10 · see citing works
“As with meaning preservation, these approaches can be used with different embeddings, for example, authorship representations (Rivera-Soto et al. 2021) and instruction fine-tuned embeddings (Su et al. 2023).”
Learning Interpretable Style Embeddings via Prompting LLMs
Patel, A., Rao, D., Kothary, A., McKeown, K. R., & Callison-Burch, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1020 · Cited by 20 · see citing works
Learning Interpretable Style Embeddings via Prompting LLMs
Patel, A., Rao, D., Kothary, A., McKeown, K., & Callison-Burch, C. (2023) · arXiv · doi:10.48550/arxiv.2305.12696 · Cited by 10 · see citing works
ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer
Horvitz, Z., Patel, A., Callison-Burch, C., Zhou, Y., & McKeown, K. R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29780 · Cited by 7 · see citing works
Tyo, J., Dhingra, B., & Lipton, Z. C. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.43 · Cited by 6 · see citing works
Validation in Forensic Text Comparison: Issues and Opportunities
Ishihara, S., Kulkarni, S., Carne, M. J., Ehrhardt, S., & Nini, A. (2024) · Languages · doi:10.3390/languages9020047 · Cited by 5 · see citing works
“The AAVC is a widely recognized corpus specifically designed for authorship verification studies, as evidenced by its utilization in various studies (Boenninghoff et al. 2019; Halvani et al. 2020; Ishihara 2023; Rivera-Soto et al. 2021).”
MethodChen, J., Guo, J., Li, X., Wang, H., Xu, Z., Gong, Z., Zhang, L., & Leung, V. C. M. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3452695 · Cited by 4 · see citing works
“UTRL is widely applicable in various domains, including sentiment analysis [12], code-switching identification [13], and authorship representations [14].”
Nini, A., Halvani, O., Graner, L., Titze, S., Gherardi, V., & Ishihara, S. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06340-3 · Cited by 4 · see citing works
Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?
Aggazzotti, C., Andrews, N., & Smith, E. A. T. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00678 · Cited by 4 · see citing works
Self-citationCan Authorship Representation Learning Capture Stylistic Features?
Wang, A., Aggazzotti, C., Kotula, R., Soto, R. A. R., Bishop, M., & Andrews, N. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00610 · Cited by 27 · see citing works
“We used Reddit because it has been shown to yield representations that generalize well to other domains (Rivera-Soto et al., 2021).”
BackgroundMethodSelf-citationAddressing Topic Leakage in Cross-Topic Evaluation for Authorship Verification
Sawatphol, J., Udomcharoenchaikit, C., & Nutanong, S. Y. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00709 · Cited by 5 · see citing works
Attributing authorship via the perplexity of authorial language models
Huang, W., Murakami, A., & Grieve, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327081 · Cited by 3 · see citing works
Topic-Regularized Authorship Representation Learning
Sawatphol, J., Chaiwong, N., Udomcharoenchaikit, C., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.70 · Cited by 3 · see citing works
“, 2019), and data augmentation (Rivera-Soto et al., 2021) have been used in conjunction with classification algorithms to reduce topic bias and improve performance on unseen topic texts.”
BackgroundMethodInfluentialContrastive Disentanglement for Authorship Attribution
Hu, Z., Nguyen, T. T., Hu, Y., Hung, C.-Y., Hee, M. S., Seah, C. W., & Lee, R. K.-W. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3652501 · Cited by 2 · see citing works
“By fine-tuning these models for specific AA tasks, studies by Rivera-Soto et al. [34], Manolache et al. [28], Reimers et al. [33], and El Boukkouri et al. [13] have demonstrated remarkable improvements in identifying authors based on their writing styles.”
Meng, D., Wang, Z., Phuntsho, T., & Gonsalves, T. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100752 · Cited by 1 · see citing works
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Aggazzotti, C., Wiesner, M., Smith, E. A., & Andrews, N. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.54 · Cited by 6 · see citing works
Self-citationCounterfactual Augmentation for Robust Authorship Representation Learning
Man, H., & Nguyen, T. H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657956 · Cited by 1 · see citing works
“Untangling distinctive stylistic elements of an author’s writing from their content has proven to be an exceptionally difficult task in computational linguistics over the years [20, 29].”
InfluentialUsing Contrastive Learning to Map Stylistic Similarities in Narrative Writers
Redondo, M. V., Huertas‐Tato, J., Maceiras, S. D., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77731-8_6
Romanov, A., Kurtukova, A., Fedotova, A., & Shelupanov, A. A. (2025) · IEEE Access · doi:10.1109/access.2025.3584616
“The paper [24] addresses the problem of cross-domain transfer.”
Man, H., Ngo, N. T., Lai, V. D., Rossi, R. A., Dernoncourt, F., & Nguyen, T. H. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730029
“…LLM embeddings in downstream tasks, ranging from deep context understanding requirements like sentiment analysis [18] to text style comprehension tasks such as authorship attribution [38, 51], thereby contributing to the advancement of natural language processing and information retrieval fields.”
Self-Supervised Learning of Behavioral User Representations for Authorship Similarity Analysis
Aslanzadeh, S., Bourgeois, A. G., & Kubek, M. (2026) · doi:10.1109/icnlp69856.2026.11527661
“Rivera-Soto et al. demonstrate that authorship representations can transfer across domains, albeit unevenly, highlighting the need for methods that encourage invariance to topic while preserving stylistic cues [4].”
Five Myths About Influence Operations: What 25 Million Tweets Across Seven State Campaigns Reveal
Ferrara, E. (2026) · Systems · doi:10.3390/systems14080970
- Authors
- Rivera-Soto, R. A., Miano, O. E., Ordonez, J., Chen, B. Y., Khan, A., Bishop, M., & Andrews, N.
- Affiliations
- Lawrence Livermore National Laboratory (US)
- Johns Hopkins University (US)
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 913-919
- OpenAlex
- W4205619213
- Semantic Scholar
- c71f40033cb0f8e76ef28e504240ae14317a5094
- Cited by
- 25
- References
- 23
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 2
- FWCI
- 1.96
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- U.S. Department of Energy (DEAC5207NA27344)
- Johns Hopkins University
- Lawrence Livermore National Laboratory (DE‐AC5207NA27344)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- representation learning
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