Abstract
Exploring audience engagement with digital media content may lead to many various benefits. In this paper, we study how adding engagement-based features to the article description can influence the efficiency of algorithms aimed at detecting digital media readers’ propensity to buy a subscription. Based on the propensity score, the publishers can optimize a decision to display a paywall. Moreover, it is observed that more and more page views are of new or anonymous users. Consequently, the decision concerning the paywall application has to rely only on digital content features. In order to address this application scenario, we propose a novel digital content enrichment framework based on the engagement statistics of users reading a given article. We experimentally evaluate the performance of machine learning algorithms for predicting the propensity to subscribe using the dataset based on events describing the behavior of users exploring the digital news site of the large media publisher. The results of experiments demonstrate that enrichment of article profiles with user engagement features significantly improves prediction models’ efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carlton, J., Brown, A., Jay, C., Keane, J.: Using Interaction Data to Predict Engagement with Interactive Media, pp. 1258–1266. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3474085.3475631
Davoudi, H., Rashidi, Z., An, A., Zihayat, M., Edall, G.: Paywall policy learning in digital news media. IEEE Trans. Knowl. Data Eng. 33(10), 3394–3409 (2021). https://doi.org/10.1109/TKDE.2020.2969419
Davoudi, H., Zihayat, M., An, A.: Time-aware subscription prediction model for user acquisition in digital news media. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 135–143. SIAM (2017). https://doi.org/10.1137/1.9781611974973.16
Dorogush, A.V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L.O., Vorobev, A.: Fighting biases with dynamic boosting. CoRR abs/1706.09516 (2017). http://arxiv.org/abs/1706.09516
Fei, Y., Lv, C., Feng, Y., Zhao, D.: Real-time filtering on interest profiles in twitter stream. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, pp. 263–264. JCDL 2016, ACM, New York, NY, USA (2016). https://doi.org/10.1145/2910896.2925462
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York, NY, USA (2012)
Grinberg, N.: Identifying modes of user engagement with online news and their relationship to information gain in text. In: Proceedings of the 2018 World Wide Web Conference, pp. 1745–1754. WWW ’18 (2018). https://doi.org/10.1145/3178876.3186180
Li, H., Vu, Q.H., Pham, T.L., Nguyen, T.T., Chen, S., Lee, S.: An ensemble approach to streaming service churn prediction. In: WSDM Cup 2018 Workshop, The 11th ACM International Conference on Web Search and Data Mining, Los Angeles, California, USA, pp. 1–8 (2018). https://wsdm-cup-2018.kkbox.events/
Liang, S.: Collaborative, dynamic and diversified user profiling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 4269–4276 (2019). https://doi.org/10.1609/aaai.v33i01.33014269
Madnani, N., Loukina, A., Cahill, A.: A large scale quantitative exploration of modeling strategies for content scoring. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 457–467 (2017). https://doi.org/10.18653/v1/W17-5052
Misiorek, P., Warmuz, J., Kaczmarek, D., Ciesielczyk, M.: Modeling user engagement profiles for detection of digital subscription propensity. In: Themistocleous, M., Papadaki, M. (eds.) Information Systems. EMCIS 2021. LNBIP, vol. 437, pp. 55–68. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-95947-0_5
Riordan, B., Flor, M., Pugh, R.: How to account for mispellings: quantifying the benefit of character representations in neural content scoring models. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 116–126 (2019). https://doi.org/10.18653/v1/W19-4411
Tang, X., Xu, Y., Geva, S.: Integrating time forgetting mechanisms into topic-based user interest profiling. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 1–4 (2013). https://doi.org/10.1109/WI-IAT.2013.132
Yandex: Catboost - open-source gradient boosting library. https://catboost.ai/. Accessed October 2022
Acknowledgments
This work was supported by the Polish National Centre for Research and Development, grant POIR.01.01.01-00-1352/17-00 and by Poznan University of Technology, grant 0311/SBAD/0727.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Misiorek, P., Ciesielczyk, M., Rzycki, B. (2023). Digital Content Profiling Based on User Engagement Features. In: Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-30694-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-30694-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30693-8
Online ISBN: 978-3-031-30694-5
eBook Packages: Computer ScienceComputer Science (R0)