ISSN : 2583-2646

Redefining Brand Safety in Programmatic Advertising: Machine Learning Approaches to Content Analysis

ESP Journal of Engineering & Technology Advancements
© 2021 by ESP JETA
Volume 1  Issue 2
Year of Publication : 2021
Authors : Ankush Singhal
: 10.56472/25832646/JETA-V1I2P124

Citation:

Ankush Singhal, 2021. "Redefining Brand Safety in Programmatic Advertising: Machine Learning Approaches to Content Analysis", ESP Journal of Engineering & Technology Advancements 1(2): 231-243.

Abstract:

Today, digital marketing has transformed, as programmatic advertising has become the new normal, allowing advertisers to deliver their campaigns to the right audiences at scale. Yet, in an ever-changing digital landscape, brand safety remains a challenge. Most traditional brand safety mechanisms fail to do the job of context well, leading to either missed opportunities or inappropriate placements that destroy the brand’s reputation. In this paper, we explore using machine learning to redefine brand safety in programmatic advertising through the process of content analysis. In this work, we analyze the use of Natural Language Processing (NLP), computer vision, and sentiment analysis to gauge content quality and context across various platforms. Through case studies and real-world use, this paper demonstrates how machine learning might create a more nuanced, adaptable, and effective brand safety framework. The findings also highlight the critical need for AI-powered content analysis to protect brands and build consumer trust in digital advertising.

References:

[1] Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).

[2] Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., ... & Stoica, I. (2012). Resilient distributed datasets: A {Fault-Tolerant} abstraction for {In-Memory} cluster computing. In 9th USENIX symposium on networked systems design and implementation (NSDI 12) (pp. 15-28).

[3] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 785-794).

[4] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.

[5] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[6] Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3128-3137).

[7] Mikolov, T. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 3781.

[8] Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., ... & Ng, A. (2012). Large scale distributed deep networks. Advances in neural information processing systems, 25.

[9] Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).

[10] Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.

[11] Devlin, J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

[12] Zvelo, URL Database for Brand Safety & Contextual Targeting, Protect Your Identity and the Placement Of Digital Advertisements, online. https://zvelo.com/solutions/brand-safety-contextual-targeting/

[13] How Machine Learning Advertising Improve Ad Campaigns, Smartyads, 2022. online. https://smartyads.com/blog/how-machine-learning-would-improve-your-ads

[14] Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 3-11). Springer International Publishing.

[15] Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A Netflix case study. In Recommender Systems Handbook (pp. 385-419). Boston, MA: Springer US.

[16] Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459.

[17] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[18] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.

[19] Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).

[20] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.

[21] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

[22] Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.

[23] Ng, A., & Jordan, M. (2001). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. Advances in neural information processing systems, 14.

[24] Bengio, Y., Ducharme, R., & Vincent, P. (2000). A neural probabilistic language model. Advances in neural information processing systems, 13.

[25] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

[26] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536.

Keywords:

Programmatic Advertising, Brand Safety, Machine Learning, Content Analysis, Natural Language Processing, Computer Vision, Sentiment Analysis, Contextual Analysis.