Vol. 4 No. 2 (2024): July−December 2024
Articles

Efficient Visual Content Analysis for Social Media Advertising Performance Assessment

View PDF
  • Yizhe Chen,
  • Yingqi Zhang,
  • Xuzhong Jia
Yizhe Chen
Computer Science, University of California, San Diego, CA, USA
Yingqi Zhang
Computer Science, Carnegie Mellon University, CA, USA
Xuzhong Jia
Computer Application Technology, Hunan University of Technology, HuNan, China

Published 2024-07-30

Keywords

  • Visual content analysis,
  • computational advertising,
  • lightweight deep learning,
  • multi-modal fusion

How to Cite

Chen, Y., Zhang, Y., & Jia, X. (2024). Efficient Visual Content Analysis for Social Media Advertising Performance Assessment. Spectrum of Research, 4(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/11

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper proposes an efficient visual content analysis framework for social media advertising performance assessment. The framework integrates hierarchical visual perception learning with multi-modal content analysis to achieve an optimal balance between computational efficiency and analytical accuracy. A novel feature extraction architecture employs cascaded attention mechanisms to capture multi-scale visual information while maintaining minimal computational overhead. The framework incorporates a specialized performance assessment module that evaluates multiple advertising metrics simultaneously through an adaptive fusion mechanism. Extensive experiments on a large-scale dataset of 100,000 advertising images demonstrate that the proposed approach achieves 89.2% accuracy in performance prediction while reducing processing latency by 35.2% compared to state-of-the-art methods. The system processes content at 325.6 frames per second on standard GPU hardware, making it suitable for real-time advertising analysis applications. Comparative analysis shows that the framework outperforms existing methods across multiple performance metrics, achieving a 12.5% improvement in Mean Average Precision while requiring 42.1% less memory. The implementation demonstrates robust performance across diverse advertising categories and social media platforms, establishing new benchmarks in lightweight visual content analysis for computational advertising.

View PDF

References

  1. Ju, C., Liu, Y., & Shu, M. (2024). Performance evaluation of supply chain disruption risk prediction models in healthcare: A multi-source data analysis.
  2. Du, P., Li, X., & Gao, Y. (2020, June). Employ multimodal machine learning for content quality analysis. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (Vol. 1, pp. 2658-2661). IEEE.
  3. Diaz, D., Moreno-Vera, F., Heredia, J., Venturim, F., & Poco, J. (2024, October). FishBiasLens: Integrating Large Language Models and Visual Analytics for Bias Detection. In 2024 IEEE Visual Analytics Science and Technology VAST Challenge (pp. 17-18). IEEE.
  4. Liu, Y., Gu, Y. C., Zhang, X. Y., Wang, W., & Cheng, M. M. (2020). Lightweight salient object detection via hierarchical visual perception learning. IEEE transactions on cybernetics, 51(9), 4439-4449.
  5. Kuang, W., Chan, Y. L., & Tsang, S. H. (2019, December). Efficient mode decision for HEVC screen content coding by content analysis. In 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) (pp. 1-5). IEEE.
  6. Xia, S., Zhu, Y., Zheng, S., Lu, T., & Ke, X. (2024). A Deep Learning-based Model for P2P Microloan Default Risk Prediction. International Journal of Innovative Research in Engineering and Management, 11(5), 110-120.
  7. Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.
  8. Liu, Y., Xu, Y., & Zhou, S. (2024). Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design. Authorea Preprints.
  9. Xu, Y., Liu, Y., Wu, J., & Zhan, X. (2024). Privacy by Design in Machine Learning Data Collection: An Experiment on Enhancing User Experience. Applied and Computational Engineering, 97, 64-68.
  10. Xu, X., Xu, Z., Yu, P., & Wang, J. (2025). Enhancing User Intent for Recommendation Systems via Large Language Models. Preprints.
  11. Li, L., Xiong, K., Wang, G., & Shi, J. (2024). AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure. Journal of Theory and Practice of Engineering Science, 4(12), 33-47.
  12. Yu, P., Xu, X., & Wang, J. (2024). Applications of Large Language Models in Multimodal Learning. Journal of Computer Technology and Applied Mathematics, 1(4), 108-116.
  13. Chen, Y., Feng, E., & Ling, Z. (2024). Secure Resource Allocation Optimization in Cloud Computing Using Deep Reinforcement Learning. Journal of Advanced Computing Systems, 4(11), 15-29.
  14. Shen, Q., Zhang, Y., & Xi, Y. (2024). Deep Learning-Based Investment Risk Assessment Model for Distributed Photovoltaic Projects. Journal of Advanced Computing Systems, 4(3), 31-46.
  15. Chen, J., Zhang, Y., & Wang, S. (2024). Deep Reinforcement Learning-Based Optimization for IC Layout Design Rule Verification. Journal of Advanced Computing Systems, 4(3), 16-30.
  16. Ju, C. (2023). A Machine Learning Approach to Supply Chain Vulnerability Early Warning System: Evidence from US Semiconductor Industry. Journal of Advanced Computing Systems, 3(11), 21-35.
  17. Ju, C., & Ma, X. (2024). Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach. International Journal of Computer and Information Systems (IJCIS), 5(1), 103-114.
  18. Xiong, K., Wu, Z., & Jia, X. (2025). DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments. Journal of Advanced Computing Systems, 5(1), 1-17.
  19. Wang, S., Hu, C., & Jia, G. (2024). Deep Learning-Based Saliency Assessment Model for Product Placement in Video Advertisements. Journal of Advanced Computing Systems, 4(5), 27-41.
  20. Pu, Y., Chen, Y., & Fan, J. (2023). P2P Lending Default Risk Prediction Using Attention-Enhanced Graph Neural Networks. Journal of Advanced Computing Systems, 3(11), 8-20.
  21. Jin, M., Zhang, H., & Huang, D. (2024). Deep Learning-Based Early Warning Model for Continuous Glucose Monitoring Data in Diabetes Management. Integrated Journal of Science and Technology, 1(2).
  22. Ma, X., & Jiang, X. (2024). Predicting Cross-border E-commerce Purchase Behavior in Organic Products: A Machine Learning Approach Integrating Cultural Dimensions and Digital Footprints. International Journal of Computer and Information System (IJCIS), 5(1), 91-102.
  23. Xiong, K., Cao, G., Jin, M., & Ye, B. (2024). A Multi-modal Deep Learning Approach for Predicting Type 2 Diabetes Complications: Early Warning System Design and Implementation.
  24. Fan, J., Trinh, T. K., & Zhang, H. (2024). Deep Learning-Based Transfer Pricing Anomaly Detection and Risk Alert System for Pharmaceutical Companies: A Data Security-Oriented Approach. Journal of Advanced Computing Systems, 4(2), 1-14.
  25. Xi, Y., Jia, X., & Zhang, H. (2024). Real-time Multimodal Route Optimization and Anomaly Detection for Cross-border Logistics Using Deep Reinforcement Learning. International Journal of Computer and Information System (IJCIS), 5(2), 102-114.
  26. Chen, J., & Wang, S. (2024). A Deep Reinforcement Learning Approach for Network-on-Chip Layout Verification and Route Optimization. International Journal of Computer and Information System (IJCIS), 5(1), 67-78.
  27. Jia, X., Zhang, H., Hu, C., & Jia, G. (2024). Joint Enhancement of Historical News Video Quality Using Modified Conditional GANs: A Dual-Stream Approach for Video and Audio Restoration. International Journal of Computer and Information System (IJCIS), 5(1), 79-90.
  28. Zhang, H., Jia, X., & Chen, C. (2025). Deep Learning-Based Real-Time Data Quality Assessment and Anomaly Detection for Large-Scale Distributed Data Streams.
  29. Wang, J., Zhao, Q., & Xi, Y. (2025). Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior. Annals of Applied Sciences, 6(1).
  30. Ju, C., Shen, Q., & Ni, X. (2024). Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets. Applied and Computational Engineering, 112, 47-53.
  31. Ju, C., Liu, Y., & Shu, M. (2024). Performance evaluation of supply chain disruption risk prediction models in healthcare: A multi-source data analysis.
  32. Ma, D., Jin, M., Zhou, Z., Wu, J., & Liu, Y. (2024). Deep Learning-Based ADL Assessment and Personalized Care Planning Optimization in Adult Day Health Center. Applied and Computational Engineering, 118, 14-22.
  33. Wei, M., Wang, S., Pu, Y., & Wu, J. (2024). Multi-Agent Reinforcement Learning for High-Frequency Trading Strategy Optimization. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 2(1), 109-124.
  34. Wen, X., Shen, Q., Wang, S., & Zhang, H. (2024). Leveraging AI and Machine Learning Models for Enhanced Efficiency in Renewable Energy Systems. Applied and Computational Engineering, 96, 107-112.
  35. Yan, L., Zhou, S., Zheng, W., & Chen, J. (2024). Deep Reinforcement Learning-based Resource Adaptive Scheduling for Cloud Video Conferencing Systems.
  36. Chen, J., Yan, L., Wang, S., & Zheng, W. (2024). Deep Reinforcement Learning-Based Automatic Test Case Generation for Hardware Verification. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 409-429.
  37. Yan, L., Wang, Y., Guo, L., & Qian, K. (2025). Enhanced Spatio-Temporal Attention Mechanism for Video Anomaly Event Detection. Applied and Computational Engineering, 117, 155-164.
  38. Yu, P., Xu, Z., Wang, J., & Xu, X. (2025). The Application of Large Language Models in Recommendation Systems. arXiv preprint arXiv:2501.02178.
  39. Yi, J., Xu, Z., Huang, T., & Yu, P. (2025). Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions. arXiv preprint arXiv:2502.00339.
  40. Huang, T., Xu, Z., Yu, P., Yi, J., & Xu, X. (2025). A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit. arXiv preprint arXiv:2502.09097.
  41. Wang, J., Xu, X., Yu, P., & Xu, Z. (2025). Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media.
  42. Huang, T., Yi, J., Yu, P., & Xu, X. (2025). Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies.
  43. Liang, X., & Chen, H. (2024, July). One cloud subscription-based software license management and protection mechanism. In Proceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering (pp. 199-203).
  44. Xu, J., Wang, Y., Chen, H., & Shen, Z. (2025). Adversarial Machine Learning in Cybersecurity: Attacks and Defenses. International Journal of Management Science Research, 8(2), 26-33.
  45. Chen, H., Shen, Z., Wang, Y., & Xu, J. (2024). Threat Detection Driven by Artificial Intelligence: Enhancing Cybersecurity with Machine Learning Algorithms.
  46. Xu,J.;Chen,H.;Xiao,X.;Zhao,M.;Liu,B. (2025).Gesture Object Detection and Recognition Based on YOLOv11.Applied and Computational Engineering,133,81-89.
  47. Ma, X., & Fan, S. (2024). Research on Cross-national Customer Churn Prediction Model for Biopharmaceutical Products Based on LSTM-Attention Mechanism. Academia Nexus Journal, 3(3).
  48. Bi, W., Trinh, T. K., & Fan, S. (2024). Machine Learning-Based Pattern Recognition for Anti-Money Laundering in Banking Systems. Journal of Advanced Computing Systems, 4(11), 30-41.