Predictive Models of Typographic Preference in Digital Media

Authors

DOI:

https://doi.org/10.56294/dm20251062

Keywords:

Typography, Predictive Modeling, Digital Media, User Preferences, Font Psychology

Abstract

Introduction: This article explores how typography influences user experience in digital environments, highlighting its evolution from the 11th century to the Internet era. Objective: The aim of this research was to examine the psychological impact of fonts, which evoke emotional responses and affect readability, design and user behavior. Methodology: Predictive models, such as regression, classification and time series, are used to analyze typographic preferences, helping designers to optimize digital interfaces. Results: The study simulated data from 1,000 participants, considering variables such as age, gender, educational level and context of use, revealing a predominant preference for Sans Serif typefaces (63.3%), especially in academic reading. The Logistic Regression and SVM models showed a moderate performance (accuracy of 0.627 and 0.634), with better ability to identify preferences for Sans Serif, although with limitations for the minority class (Serif). Conclusion: It was concluded that psychological, cultural and contextual factors significantly influence preferences, highlighting the need to integrate these variables in future models to improve accuracy and personalization in digital design.

References

1. Bai, Y., Huang, Z., Gao, W., Yang, S., & Liu, J. (2024). Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation. APSIPA Transactions on Signal and Information Processing, 13(1). https://doi.org/10.48550/arXiv.2407.14774

2. Iluz, S., Vinker, Y., Hertz, A., Berio, D., Cohen-Or, D., & Shamir, A. (2023). Word-as-image for semantic typography. ACM Transactions on Graphics (TOG), 42(4), 1-11. http://dx.doi.org/10.48550/arXiv.2303.01818

3. Günay, M. (2024). The Impact of Typography in Graphic Design. International Journal of Eurasia Social Sciences, 15(57), 1446-1464. http://dx.doi.org/10.35826/ijoess.4519

4. Tanveer, M., Wang, Y., Mahdavi-Amiri, A., & Zhang, H. (2023). Ds-fusion: Artistic typography via discriminated and stylized diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 374-384). http://dx.doi.org/10.48550/arXiv.2303.09604

5. Kim, S., Jung, A. R., & Kim, Y. (2021). The effects of typefaces on ad effectiveness considering psychological perception and perceived communicator’s power. Journal of Marketing Communications, 27(7), 716-741.

http://dx.doi.org/10.15444/GMC2018.08.08.05

6. Brown, N. B. (2024). The Cognitive Type Project--Mapping Typography to Cognition. arXiv preprint arXiv:2403.04087. https://doi.org/10.48550/arXiv.2403.04087

7. Ezzeldeen, O. (2024). The Abstract Character of Typography Art. Journal of Design Sciences and Applied Arts, 5(2), 40-48.

http://dx.doi.org/10.21608/jdsaa.2024.229906.1365

8. Poon, S. T. (2021). Typography design’s new trajectory towards visual literacy for digital

mediums. Studies in Media and Communication, 9(1), 9.

http://dx.doi.org/10.11114/smc.v9i1.5071

9. Haenschen, K., Tamul, D. J., & Collier, J. R. (2021). Font matters: Understanding typeface selection by political campaigns. International Journal of Communication, 15, 21. https://ijoc.org/index.php/ijoc/article/view/17615

10. Rosidah, S., Ivan, F. X., Murnani, S., Nurlatifa, H., Nugraha, K. A., & Wibirama, S. (2025). Impact of Color, Shape, and Typeface on Visual Attention: An Eye Tracking Study on Brand Logo. ASEAN Journal on Science and Technology for Development, 42(1), 1-17. http://dx.doi.org/10.29037/2224-9028.1608

11. Rosyid, H. A., Putra, A. Y. H., Akbar, M. I., & Dwiyanto, F. A. (2022). Can Multinomial Logistic Regression Predicts Research Group using Text Input?. Knowl. Eng. Data Sci., 5(2), 150-159. http://dx.doi.org/10.17977/um018v5i22022p150-159

12. Zhang, M., Teng, L., Xie, C., Wang, X., & Foti, L. (2025). Serif or sans serif typefaces? The effects of typefaces on consumers’ perceptions of activity and potency of brand logos. European Journal of Marketing, 59(4), 879-922. https://doi.org/10.1108/EJM- 06-2023-0497

13. Vecino, S., Gonzalez-Rodriguez, M., Fernandez-Lanvin, D., & de Andres, J. (2025). The impact of serif vs sans-serif typefaces on e-commerce websites. International Journal of Human–Computer Interaction, 41(5), 3613-3624.

http://dx.doi.org/10.1080/10447318.2024.2338667

14. Vecino, S., Mehtali, J., de Andrés, J., Gonzalez-Rodriguez, M., & Fernandez-Lanvin, D. (2022). How does serif vs sans serif typeface impact the usability of e-commerce websites?. PeerJ Computer Science, 8, e1139. http://dx.doi.org/10.7717/peerj- cs.1139

15. Minakata, K., & Beier, S. (2022). The dispute about sans serif versus serif fonts: An interaction between the variables of serif and stroke contrast. Acta Psychologica, 228, 103623. https://doi.org/10.1016/j.actpsy.2022.103623

16. Mushtaq, M., Ibrahim, A., Adeel, M., Murtaza, G., & Waleed, A. (2025). Experimental Typography Typeface Design Inspired from Dates Palm Tree Bark Texture. European Journal of Arts, Humanities and Social Sciences, 2(1), 54-59. http://dx.doi.org/10.59324/ejahss.2025.2(1).06

17. Chen, W., Yang, J., & Wang, Y. (2024). The influence of Chinese typography on information dissemination in graphic design: based on eye-tracking data. Scientific Reports, 14(1), 13947. http://dx.doi.org/10.1038/s41598-024-64964-y

18. Tatsukawa, Y., Shen, I. C., Qi, A., Koyama, Y., Igarashi, T., & Shamir, A. (2024). FontCLIP: A Semantic Typography Visual‐Language Model for Multilingual Font Applications. In Computer Graphics Forum (Vol. 43, No. 2, p. e15043). http://dx.doi.org/10.1111/cgf.15043

19. Cheng, H., Xiao, E., Gu, J., Yang, L., Duan, J., Zhang, J., ... & Xu, R. (2024). Unveiling typographic deceptions: Insights of the typographic vulnerability in large vision-language models. In European Conference on Computer Vision (pp. 179-196). Cham: Springer Nature Switzerland. http://dx.doi.org/10.1007/978-3-031-73202-7_11

20. Allal, Z., Noura, H. N., Salman, O., & Chahine, K. (2024). Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids. Engineering Applications of Artificial Intelligence, 133, 108304.

http://dx.doi.org/10.1016/j.engappai.2024.108304

21. Mosleh, M. A., & Gumaei, A. H. (2024). An Efficient Bidirectional Android Translation Prototype for Yemeni Sign Language Using Fuzzy logic and CNN Transfer Learning Models. IEEE Access. http://dx.doi.org/10.1109/ACCESS.2024.3512455

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Published

2025-06-03

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Section

Original

How to Cite

1.
Rivera-Abarca AL, García-Guerra JI, Aguilar-Cajas HO, Vergara-Zurita HE, López-Pumalema JI, Armijos-Arcos F. Predictive Models of Typographic Preference in Digital Media. Data and Metadata [Internet]. 2025 Jun. 3 [cited 2025 Jul. 13];4:1062. Available from: https://dm.ageditor.ar/index.php/dm/article/view/1062