![]()
Certificate: View Certificate
Published Paper PDF: View PDF
Confirmation Letter: View
Dr. Abu Farah Hoque
Assistant Professor, Department of English
Maharani Kasiswari College, 20
Ramkanto Bose Street, Kolkata -700003
(affiliated to the University of Calcutta, India)
ORCID ID: 0009-0008-6127-9654
Abstract — Modern English fiction increasingly presents identity as fluid, relational, contested, and continuously reconstructed rather than as a stable attribute inherited from nationality, gender, class, or cultural origin.
This study investigates how contemporary fictional narratives encode transformations of personal and social identity through character language, interpersonal relationships, spatial displacement, memory, and changing narrative positions. A major research gap exists because conventional literary scholarship primarily interprets such transformations through close reading, whereas computational literary studies frequently quantify characters without adequately modelling the temporal evolution of their identities. The proposed study therefore develops a computational-humanistic framework integrating contextual language modelling, semantic trajectory analysis, character-network analysis, and interpretable machine-learning techniques.
Modern fiction published across culturally diverse English-language literary traditions is conceptualized as a longitudinal narrative environment in which characters’ linguistic and relational profiles can be traced across successive narrative segments. The framework is designed to identify semantic identity shifts associated with belonging, alienation, gendered subjectivity, migration, social recognition, cultural hybridity, and self-redefinition.
Keywords — modern English fiction, identity representation, computational literary studies, natural language processing, character networks, semantic change
References
- Bhabha, H. K. (1994). The location of culture. Routledge.
- Brahman, F., Huang, M., Tafjord, O., Zhao, C., Sachan, M., & Chaturvedi, S. (2021). “Let your characters tell their story”: A dataset for character-centric narrative understanding. Findings of the Association for Computational Linguistics: EMNLP 2021, 1734–1752.
- Butler, J. (2006). Gender trouble: Feminism and the subversion of identity. Routledge.
- Dinu, L. P., & Uban, A. S. (2023). A computational analysis of the voices of Shakespeare’s characters. Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023), 295–301.
- Dobson, J. E. (2019). Critical digital humanities: The search for a methodology. University of Illinois Press.
- Dobson, J. E. (2022). Vector hermeneutics: On the interpretation of vector space models of text. Digital Scholarship in the Humanities, 37(1), 81–93.
- Giddens, A. (1991). Modernity and self-identity: Self and society in the late modern age. Stanford University Press.
- Hall, S. (1990). Cultural identity and diaspora. In J. Rutherford (Ed.), Identity: Community, culture, difference (pp. 222–237). Lawrence & Wishart.
- Hoque, M. N., Ghai, B., Kraus, K., & Elmqvist, N. (2023). Portrayal: Leveraging NLP and visualization for analyzing fictional characters. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (pp. 1371–1385). Association for Computing Machinery. https://doi.org/10.1145/3563657.3596000
- Jockers, M. L. (2013). Macroanalysis: Digital methods and literary history. University of Illinois Press.
- Konovalova, A., Toral, A., & Taivalkoski-Shilov, K. (2022). Dr. Livingstone, I presume? Polishing of foreign character identification in literary texts. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop (pp. 133–139). Association for Computational Linguistics.
- Moretti, F. (2013). Distant reading. Verso.
- Yu, M., Li, J., Yao, S., Pang, W., Zhou, X., Xiao, Z., Meng, F., & Zhou, J. (2023). Personality understanding of fictional characters during book reading. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 14784–14802). Association for Computational Linguistics.