Automatic Gender Identification from Text
Source abstract
Abstract
The gender identification of authors in literary texts is a compelling research area at the intersection of computational linguistics and natural language processing, offering insights into historical biases and socio-cultural dynamics while enriching our understanding of literary traditions. This study is inspired by the historical context of women adopting male pseudonyms to navigate a male-dominated literary domain. By leveraging machine learning and state-of-the-art language models, we investigate the feasibility and accuracy of inferring an author’s gender from their writings. Our key contributions include (1) the creation of a large-scale, diverse dataset of literary texts spanning various literary epochs and (2) the evaluation of multiple classification models. Our experiments reveal that the best-performing model achieves an accuracy above 90%, highlighting the potential of computational methods to uncover stylistic and linguistic markers tied to gender. These findings open avenues for further research into stylistic and linguistic patterns across literary history and their relationship to authorial identity.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Authorship Attribution and Profiling · Natural Language Processing Techniques · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Software Engineering Department, Shamoon College of Engineering, Beer Sheva 84100, Israel
INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
References (37)
Show source-provided references
1. Sand, G. (2000). Indiana, Chicago Review Press.
2. Sand, G. (2005). Valentine, Chicago Review Press.
3. Lee, V. (2017). The Tower of the Mirrors and Other Essays on the Spirit of Places (1914). By: Vernon Lee: Vernon Lee Was the Pseudonym of the British Writer Violet Paget (14 October 1856–13 February 1935), CreateSpace Independent Publishing Platform.
4. Paget, V. (1886). A Phantom Lover, by Vernon Lee, IndyPublish.com.
5. Tiptree, J. (2014). Her Smoke Rose Up Forever, Orion Publishing Group.
6. Tiptree, J. (2015). The Starry Rift, Orion Publishing Group.
7. Boyd, R.L., Ashokkumar, A., Seraj, S., and Pennebaker, J.W. (2022). The Development and Psychometric Properties of LIWC-22, University of Texas at Austin.
8. Unsupervised Discovery of Biographical Structure from Text10.1162/tacl_a_00189 ↗
9. Author gender identification from text10.1016/j.diin.2011.04.002 ↗
10. Cheng, N., Chen, X., Chandramouli, R., and Subbalakshmi, K. (April, January 30). Gender Identification from E-mails. Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, USA.
11. Silessi, S., Varol, C., and Karabatak, M. (2016, January 18–20). Identifying Gender from SMS Text Messages. Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA.10.1109/ICMLA.2016.0086 ↗
12. Bsir, B., and Zrigui, M. (2018, January 5–7). Bidirectional LSTM for Author GenderIdentification. Proceedings of the 10th International Conference, ICCCI 2018, Bristol, UK.10.1007/978-3-319-98443-8_36 ↗
13. The construction of Chinese microblog gender-specific thesauruses and user gender classification10.1007/s41109-018-0104-1 ↗
14. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.
15. Johannsen, A., Hovy, D., and Søgaard, A. (2015, January 30–31). Cross-lingual syntactic variation over age and gender. Proceedings of the Nineteenth Conference on Computational Natural Language Learning, Beijing, China.10.18653/v1/K15-1011 ↗
16. Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review
17. Jaiswal, S., Verma, A.K., and Mukherjee, A. (2024, January 6–9). Auditing Gender Analyzers on Text Data. Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, New York, NY, USA.10.1145/3625007.3627324 ↗
18. Textual Pre-Trained Models for Gender Identification Across Community Question-Answering Members10.1109/ACCESS.2023.3235735 ↗
19. Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., and Zhou, D. (2020). MobileBERT: A Compact Task-Agnostic BERT for Resource-Limited Devices. arXiv.10.18653/v1/2020.acl-main.195 ↗
20. PAN (2020, January 01). Author Profiling. Available online: https://pan.webis.de/clef17/pan17-web/author-profiling.html.
21. Pritom, R.R. (2020, January 01). Gender Recognition Dataset. Available online: https://www.kaggle.com/datasets/rashikrahmanpritom/gender-recognition-dataset.
22. Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress.10.1007/978-1-4842-4470-8 ↗
23. Demir-Kavuk, O., Akutsu, M.K.T., and Knapp, E.W. (2011, January 18–21). Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features. Proceedings of the 2011 10th International Conference on Machine Learning and Applications, Honolulu, HI, USA.10.1186/1471-2105-12-412 ↗
24. Oakes, M., Gaizauskas, R., and Fowkes, H. (2001, January 9–13). A Method Based on the Chi-Square Test for Document Classification. Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, LA, USA.10.1145/383952.384080 ↗
25. Bentéjac, C., Csörgo, A., and Martínez-Muñoz, G. (2021). A Comparative Analysis of XGBoost. arXiv.
26. Cortes, C., and Vapnik, V. (1995). Support-Vector Networks, Kluwer Academic Publishers.10.1007/BF00994018 ↗
27. Language models are unsupervised multitask learners
28. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q.V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv.
29. Liu, Y., Ott, M., Goyal, N., Duand, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2020). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv.
30. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P.J. (2023). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv.
31. Design of experiments and focused grid search for neural network parameter optimization10.1016/j.neucom.2015.12.061 ↗
32. Random Search for Hyper-Parameter Optimization
33. Comparing BERT against Traditional Machine Learning Models in Text Classification
34. OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4 Technical Report. arXiv.
35. D’Souza, J. (2024, May 01). A Review of Transformer Models. Available online: https://doi.org/10.48366/r640001.
36. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., and Bhosale, S. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv.
37. Rabaev, I., Litvak, M., Younkin, V., Campos, R., Jorge, A.M., and Jatowt, A. (2023, January 27). The Competition on Automatic Classification of Literary Epochs. Proceedings of the 1st International Workshop on Implicit Author Characterization from Texts for Search and Retrieval, IACT 2023, Taipei, Taiwan.10.1145/3539618.3591928 ↗