Quantitative Analysis and Prediction of the Popularity of Digital Media Artworks Based on Machine Learning Algorithms

Like Ma; Xiaoli Song

doi:10.54254/2755-2721/2025.27797

Applied and Computational EngineeringOpen access

Quantitative Analysis and Prediction of the Popularity of Digital Media Artworks Based on Machine Learning Algorithms

Research Article

Open Access

Quantitative Analysis and Prediction of the Popularity of Digital Media Artworks Based on Machine Learning Algorithms

Like Ma ^1* Xiaoli Song ²

¹ School of Digital Technology& Innovation Design, Jiangnan University, Jiangsu, Wuxi, 214401, China.

² School of Digital Technology& Innovation Design, Jiangnan University, Jiangsu, Wuxi, 214401, China.

^*Corresponding author: christinemal@163.com

Published on 14 October 2025

ACE Vol.193

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-239-3

ISBN (Online): 978-1-80590-240-9

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Abstract

Exploring a quantitative evaluation system for the popularity of digital media art has become a core issue that connects artistic creation, technological application and market communication. In view of the bottlenecks existing in the current algorithms, this paper proposes an LSTM algorithm optimized based on the multi-head attention mechanism. The study first conducted a correlation analysis. The results showed that the number of interactive elements had the strongest correlation with popularity, with an absolute value of the correlation coefficient reaching 0.556887. Variables such as creation time, the number of colors, and complexity scores also have a certain correlation with popularity. It can be seen that the time invested in creation, the richness of colors, and the complexity of the work will, to a certain extent, affect the popularity of the work. Taking decision tree, random forest, CatBoost, AdaBoost and XGBoost as the comparative experimental objects, in terms of various indicators, Our model performed the best in Accuracy, Recall, Precision, F1 and AUC. Its Accuracy is 0.855, which is higher than that of decision trees (0.709), random forests (0.803), CatBoost (0.786), AdaBoost (0.778), and XGBoost (0.744), with the highest overall classification accuracy. The Recall and Precision were 0.855 and 0.856 respectively, also leading other models. It performed better in identifying positive samples and the proportion of actually positive samples among those predicted to be positive. The F1 value of 0.855 is also higher than that of other models, and it has a stronger ability to balance accuracy and recall. The AUC reached 0.904, surpassing the 0.875 of random forest and the 0.876 of CatBoost, demonstrating the best ability to distinguish between positive and negative samples. In comparison, other models are slightly inferior in various indicators, especially the overall performance of the decision tree and XGBoost, which is more significantly lower than that of the Our model. This research achievement provides a more efficient method for the quantitative assessment of the popularity of digital media art. It not only offers direction for the integration of artistic creation and technological application but also provides a scientific basis for the formulation of market communication strategies.

Keywords:

Machine learning, digital media artworks, popularity quantification.

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References

[1]. Perera, Andrea, and Pumudu Fernando. "Cyberbullying detection system on social media using supervised machine learning." Procedia Computer Science 239 (2024): 506-516.

[2]. Boddu, Raja Sarath Kumar, et al. "An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing." Materials Today: Proceedings 56 (2022): 2288-2292.

[3]. Kozcuer, Cem, Anne Mollen, and Felix Bießmann. "Towards transnational fairness in machine learning: a case study in disaster response systems." Minds and Machines 34.2 (2024): 11.

[4]. Bhaskaraputra, Aryo, et al. "Systematic literature review on solving personalization problem in digital marketing using machine learning and its impact." 2022 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2022.

[5]. Xu, Yandong, and Shah Nazir. "Ranking the art design and applications of artificial intelligence and machine learning." Journal of Software: Evolution and Process 36.2 (2024): e2486.

[6]. Thylstrup, Nanna Bonde. "The ethics and politics of data sets in the age of machine learning: Deleting traces and encountering remains." Media, Culture & Society 44.4 (2022): 655-671.

[7]. Sharma, Alpana, et al. "A conceptual analysis of machine learning towards digital marketing transformation." 2022 5th international conference on contemporary computing and informatics (IC3I). IEEE, 2022.

[8]. Omran, Esraa, Estabraq Al Tararwah, and Jamal Al Qundus. "A comparative analysis of machine learning algorithms for hate speech detection in social media." Online Journal of Communication and Media Technologies 13.4 (2023): e202348.

[9]. Lannelongue, Loïc, and Michael Inouye. "Pitfalls of machine learning models for protein–protein interaction networks." Bioinformatics 40.2 (2024): btae012.

[10]. Shaikh, Asmat Ara, et al. "The role of machine learning and artificial intelligence for making a digital classroom and its sustainable impact on education during COVID-19." Materials Today: Proceedings 56 (2022): 3211-3215.

[11]. Altché, Florent, and Arnaud de La Fortelle. "An LSTM network for highway trajectory prediction." 2017 IEEE 20th international conference on intelligent transportation systems (ITSC). IEEE, 2017.

References

[1]. Perera, Andrea, and Pumudu Fernando. "Cyberbullying detection system on social media using supervised machine learning." Procedia Computer Science 239 (2024): 506-516.

[3]. Kozcuer, Cem, Anne Mollen, and Felix Bießmann. "Towards transnational fairness in machine learning: a case study in disaster response systems." Minds and Machines 34.2 (2024): 11.

[5]. Xu, Yandong, and Shah Nazir. "Ranking the art design and applications of artificial intelligence and machine learning." Journal of Software: Evolution and Process 36.2 (2024): e2486.

[6]. Thylstrup, Nanna Bonde. "The ethics and politics of data sets in the age of machine learning: Deleting traces and encountering remains." Media, Culture & Society 44.4 (2022): 655-671.

[9]. Lannelongue, Loïc, and Michael Inouye. "Pitfalls of machine learning models for protein–protein interaction networks." Bioinformatics 40.2 (2024): btae012.

Cite this article

Ma,L.;Song,X. (2025). Quantitative Analysis and Prediction of the Popularity of Digital Media Artworks Based on Machine Learning Algorithms. Applied and Computational Engineering,193,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Machine Learning and Automation

ISBN: 978-1-80590-239-3(Print) / 978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.193

ISSN: 2755-2721(Print) / 2755-273X(Online)

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