Person Identification from Text and Speech Genre Samples
Open accessAbstract
In this paper, we describe experiments conducted on identifying a person using a novel unique correlated corpus of text and audio samples of the person's communication in six genres. The text samples include essays, emails, blogs, and chat. Audio samples were collected from individual interviews and group discussions and then transcribed to text. For each genre, samples were collected for six topics. We show that we can identify the communicant with an accuracy of 71% for six fold cross validation using an average of 22,000 words per individual across the six genres. For person identification in a particular genre (train on five genres, test on one), an average accuracy of 82% is achieved. For identification from topics (train on five topics, test on one), an average accuracy of 94% is achieved. We also report results on identifying a person's communication in a genre using text genres only as well as audio genres only.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Linguistic Inquiry and Word Count (LIWC) tool uses more than 100 psychological dimensions to analyze word use, reflecting distinct language variations among different groups in specific genres and languages.
“The Linguistic Inquiry and Word Count (LIWC) tool exemplifies how automated text analysis can use more than 100 psychological dimensions to analyze word use, reflecting distinct language variations among different groups in specific genres and languages [Goldstein-Stewart et al., 2009;Pennebaker et al., 2015;Dudău and Sava, 2021].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Goldstein-Stewart et al., 2009
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Witten, I. H. (2008) · Cited by 9,115 · see citing works
Variation across Speech and Writing
Biber, D. (1988) · doi:10.1017/cbo9780511621024 · Cited by 5,227 · see citing works
Crystal, D. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9781139164771 · Cited by 1,097 · see citing works
Inference and disputed authorship : The Federalist
Mosteller, F. C., & Wallace, D. K. (1964) · Medical Entomology and Zoology · Cited by 916 · see citing works
Automatically Categorizing Written Texts by Author Gender
Koppel, M. (2002) · Literary and Linguistic Computing · doi:10.1093/llc/17.4.401 · Cited by 623 · see citing works
Accommodating the elderly: Invoking and extending a theory
Coupland, N., Coupland, J., Giles, H., & Henwood, K. L. (1988) · Language in Society · doi:10.1017/s0047404500012574 · Cited by 534 · see citing works
Mining e-mail content for author identification forensics
de Vel, O., Anderson, A., Corney, M., & Mohay, G. (2001) · ACM SIGMOD Record · doi:10.1145/604264.604272 · Cited by 487 · see citing works
The Characteristic Curves of Composition
Mendenhall, T. C. (1887) · Science · doi:10.1126/science.ns-9.214s.237 · Cited by 407 · see citing works
Two variants of an electronic message schema
Herring, S. C. (1996) · Pragmatics & beyond. New series · doi:10.1075/pbns.39.08her · Cited by 312 · see citing works
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines
Platt, J. C. (1998) · Cited by 261 · see citing works
Gender and Power in Online Communication
Herring, S. C. (2001) · IUScholarWorks (Indiana University) · Cited by 206 · see citing works
Predicting gender from electronic discourse
Thomson, R., & Murachver, T. (2001) · British Journal of Social Psychology · doi:10.1348/014466601164812 · Cited by 179 · cited 1× · see citing works
“The context of communication influences language style (Thomson and Murachver, 2001; Coupland, et al., 1988).”
BackgroundAuthorship analysis: identifying the author of a program
Krsul, I., & Spafford, E. H. (1997) · Computers & Security · doi:10.1016/s0167-4048(97)00005-9 · Cited by 164 · see citing works
The myth of the double-blind review?: author identification using only citations
Hill, S., & Provost, F. (2003) · SKDD · doi:10.1145/980972.981001 · Cited by 124 · see citing works
The functionality attribute of cybergenres
Shepherd, M. A., & Watters, C. R. (2003) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.1999.772650 · Cited by 106 · see citing works
Analyzing Writing Styles with Coh-Metrix.
McCarthy, P. M., Lewis, G. A., Dufty, D. F., & McNamara, D. S. (2006) · Cited by 103 · see citing works
The Characteristic Curves of Composition
Eddy, H. T. (1887) · Science · doi:10.1126/science.ns-9.216.297.a · Cited by 63 · see citing works
Automatic Identification of Genre in Web Pages
Santini, M. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 55 · see citing works
Managing Content With Automatic Document Classification
Calvo, R. A., Lee, J.-M., & Li, X. (2004) · Texas Digital Library (University of Texas) · Cited by 37 · see citing works
Baron, N. S. (2004) · Routledge eBooks · doi:10.4324/9780203696965-16 · Cited by 37 · see citing works
Genre identification and goal-focused summarization
Goldstein, J., Ciany, G. M., & Carbonell, J. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321568 · Cited by 8 · see citing works
Creating and Using a Correlated Corpus to Glean Communicative Commonalities
Goldstein-Stewart, J., Goodwin, K. A., Sabin, R. E., & Winder, R. K. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3jwidme9hwfx · Cited by 6 · see citing works
Self-citationGender Differences across Correlated Corpora: Preliminary Results
Sabin, R. E., Goodwin, K. A., Goldstein-Stewart, J., & Pereira, J. A. (2008) · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
2 of the 28 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Authorship Attribution Using Text Distortion
Stamatatos, E. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1107 · Cited by 98 · see citing works
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Uchendu, A., Ma, Z., Le, T., Zhang, R., & Lee, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.172 · Cited by 82 · 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 · see citing works
Masking 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 · 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
Cross-Topic Authorship Attribution: Will Out-Of-Topic Data Help?
Sapkota, U., Solorio, T., Bethard, S. J., & Rosso, P. (2015) · Cited by 34 · see citing works
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning
Sapkota, U., Solorio, T., Montes, M., & Bethard, S. J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1210 · Cited by 18 · see citing works
A transfer learning approach to cross-domain authorship attribution
Barlas, G., & Stamatatos, E. (2021) · Evolving Systems · doi:10.1007/s12530-021-09377-2 · Cited by 15 · see citing works
Representation learning of writing style
Hay, J., Doan, B.-L., Popineau, F., & Elhara, O. A. (2020) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/2020.wnut-1.30 · Cited by 14 · see citing works
Litvinova, T., Litvinova, O., & Seredin, P. V. (2018) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct.2018.8588092 · Cited by 11 · see citing works
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution
Altakrori, M., Cheung, J. C. K., & Fung, B. C. M. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.359 · Cited by 11 · see citing works
Shedding Light on the Dark Web: Authorship Attribution in Radical Forums
Ranaldi, L., Ranaldi, F., Fallucchi, F., & Zanzotto, F. M. (2022) · Information · doi:10.3390/info13090435 · Cited by 9 · see citing works
Diaz-Asper, C. M., Bongo, L. A., & Elvevåg, B. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01987-3 · Cited by 5 · 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
Same person, different platform
Marko, K., Reitbauer, M., & Pickl, G. (2022) · Register Studies · doi:10.1075/rs.22006.mar · Cited by 4 · see citing works
Multi-Task Learning for Authorship Attribution via Topic Approximation and Competitive Attention
Song, W., Zhao, C., & Liu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2957152 · Cited by 4 · see citing works
Altakrori, M. H., Scialom, T., Fung, B. C. M., & Cheung, J. C. K. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.153 · Cited by 2 · see citing works
User identification based on short text using recurrent deep learning
Hallawi, H., Kadhim, H. R., Abdullah, Z. N., Al-Shakarchy, N. D., & Nasrawi, D. A. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i4.pp1812-1820 · Cited by 1 · see citing works
Remote data collection in sociolinguistics: lessons from the COVID-19 pandemic
Heini, A., & Kredens, K. (2023) · International Journal of Social Research Methodology · doi:10.1080/13645579.2023.2265257 · Cited by 1 · see citing works
Epistemic Modality Constructions as Stable Idiolectal Features: A Cross-genre Study of Spanish
Batel, A. M., Bonafont, M. A. S., & Kredens, K. (2023) · International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique · doi:10.1007/s11196-023-10056-5 · Cited by 1 · see citing works
Estabilidad idiolectal del español a través de cuatro géneros de comunicación
Buera, N. A., & Kredens, K. (2023) · Revista de llengua i dret · doi:10.58992/rld.i79.2023.3951
- Authors
- Goldstein-Stewart, J., Winder, R., & Sabin, R. E.
- Affiliations
- Loyola University Maryland (US)
- Venue
- Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09
- Published
- 2009
- Pages
- 336-344
- OpenAlex
- W2091612886
- Cited by
- 22
- References
- 28
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Conference of the European Chapter of the Association for Computational Linguistics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- speaker identification, personal identification, textual genres, discursive genres, chat, group discussion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review