Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

References

• Abbasi, A.; Albrecht, C.; Vance, A.; and Hansen, J. Metafraud: Ameta-learning framework for detecting financial fraud. MIS Quarterly, 36, 4 (2012), 1293–1327.
   Web of Science ®Google Scholar
• Abbasi, A.; Zhang, Z.; Zimbra, D.; Chen, H.; and Nunamaker, J.F. Detecting fake websites: The contribution of statistical learning theory. MIS Quarterly, 34, 3 (2010), 435–461.
   Web of Science ®Google Scholar
• Abbasi, A.; Zhou, Y.; Deng, S.; and Zhang, P. Text analytics to support sense-making in social media: A language-action perspective. MIS Quarterly, 42, 2 (2018), 427–464.
   Web of Science ®Google Scholar
• Adams, D.A.; Nelson, R.R.; and Todd, P.A. Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 2 (1992), 227–247.
   Web of Science ®Google Scholar
• Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., and Hinton, G.E. Neural additive models: Interpretable machine learning with neural nets. In Advances in Neural Information Processing Systems, 2021, 34, pp. 4699–4711.
   Google Scholar
• Akpinar, E.; and Berger, J. Valuable virality. Journal of Marketing Research, 54, 2 (2017), 318–330.
   Web of Science ®Google Scholar
• Alqahtani, H.; Kavakli-Thorne, M.; and Kumar, G. Applications of Generative Adversarial Networks (GANs): An updated review. Archives of Computational Methods in Engineering, 28, (2021), 525–552.
   Web of Science ®Google Scholar
• Bastani, O.; Kim, C.; and Bastani, H. Interpreting blackbox models via model extraction. arXiv, arXiv preprint arXiv:1705.08504. (2017).
   Google Scholar
• Burel, G.; Saif, H.; and Alani, H. Semantic wide and deep learning for detecting crisis-information categories on social media. In International Semantic Web Conference. 2017, pp. 138–155.
   Google Scholar
• Caruana, R.; Lou, Y.; Gehrke, J.; Koch, P.; Sturm, M.; and Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of KDD, (2015), 1721–1730.
   Google Scholar
• Chai, S.; Das, S.; and Rao, H.R. Factors affecting bloggers’ knowledge sharing: An investigation across gender. Journal of Management Information Systems, 28, 3 (2014), 309–342.
   Google Scholar
• Chai, Y.; Li, W.; Zhu, B.; Liu, H.; and Jiang, Y. An interpretable wide and deep model for online disinformation detection. SSRN Electronic Journal, SSRN 3879632 (2022).
   Google Scholar
• Cheng, H.T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; Anil, R. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 2016, pp. 7–10.
   Google Scholar
• Cichy, P.; Salge, T.O.; and Kohli, R. Privacy concerns and data sharing in the internet of things: Mixed methods evidence from connected cars. MIS Quarterly, 45, 4 (2021), 1863–1891.
   Web of Science ®Google Scholar
• Croitoru, F.-A.; Hondru, V.; Ionescu, R.T.; and Shah, M. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 8 (2022), 1–22.
   Google Scholar
• Dhurandhar, A.; Iyengar, V.; Luss, R.; and Shanmugam, K. TIP: Typifying the interpretability of procedures. arXiv, preprint arXiv:1706.02952. (June 2017).
   Google Scholar
• Dong, L.I.U.; Yue, L.I.; Jianping, L.I.N.; Houqiang, L.I.; and Feng, W.U. Deep learning-based video coding: A review and a case study. ACM Computing Surveys, 53, 1 (2019), 1–35.
   Web of Science ®Google Scholar
• Duan, W.; Gu, B.; and Whinston, A.B. The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. Journal of Retailing, 84, 2 (2008), 233–242.
   Web of Science ®Google Scholar
• Ebrahimi, M.; Chai, Y.; Samtani, S., and Chen, H. Cross-lingual cybersecurity analytics in the international dark web with adversarial deep representation learning. MIS Quarterly, 46, 2 (2022) 1209–1226.
   Web of Science ®Google Scholar
• Ebrahimi, M.; Nunamaker, J.F.; and Chen, H. Semi-supervised cyber threat identification in dark net markets: A Transductive and deep learning approach. Journal of Management Information Systems, 37, 3 (2020), 694–722.
   Web of Science ®Google Scholar
• Fang, X.; Hu, P.J.-H.H.; Li, Z. (Lionel) L.; and Tsai, W. Predicting adoption probabilities in social networks. Information Systems Research, 24, 1 (2013), 128–145.
   Web of Science ®Google Scholar
• Ferguson, R. Word of mouth and viral marketing: Taking the temperature of the hottest trends in marketing. Journal of Consumer Marketing, 25, 3 (2008), 179–182.
   Google Scholar
• Fortuna, G.; Schiavo, J.H.; Aria, M.; Mignogna, M.D.; and Klasser, G.D. The usefulness of YouTubeTM videos as a source of information on burning mouth syndrome. Journal of oral rehabilitation, 46, 7 (2019), 657–665.
   PubMed Web of Science ®Google Scholar
• Garnica-Caparrós, M.; and Memmert, D. Understanding gender differences in professional European football through machine learning interpretability and match actions data. Scientific Reports, 11, 1 (2021), 1–14.
   PubMed Web of Science ®Google Scholar
• Goldstein, J.M.; Hofman, D.G.; Wortman Vaughan, J.; Poursabzi-Sangdeh, F.; Goldstein, D.G.; Hofman, J.M.; and Wallach, H. Manipulating and measuring model interpretability. In Conference on Human Factors in Computing Systems. 2021, pp. 1–52.
   Google Scholar
• Goyal, Y.; Feder, A.; Shalit, U.; and Kim, B. Explaining Classifiers with Causal Concept Effect (CaCE). arXiv, preprint arXiv:1907.07165 (July 2019).
   Google Scholar
• Gregor, S.; and Hevner, A. Positioning and presenting design science research for maximum impact. MIS Quarterly, 37, 2 (2013), 337–355.
   Web of Science ®Google Scholar
• Guo, M.; Zhang, Q.; Liao, X.; and Zeng, D.D. An interpretable neural network model through piecewise linear approximation. arXiv, preprint arXiv:2001.07119 (2020).
   Google Scholar
• Han, Y.; Chen, W.; Xiong, X.; Li, Q.; Qiu, Z.; and Wang, T. Wide & deep learning for improving named entity recognition via text-aware named entity normalization. In Thirty-Third AAAI Conference on Artificial Intelligence. 2019.
   Google Scholar
• Heider, F.; and Simmel, M. An experimental study of apparent behavior. The American Journal of Psychology, 57, 2 (1944), 243–259.
   Web of Science ®Google Scholar
• Hevner, A.R.; March, S.T.; Park, J.; and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.
   Web of Science ®Google Scholar
• Jaakonmäki, R.; Müller, O.; and vom Brocke, J. The impact of content, context, and creator on user engagement in social media marketing. In HICSS. 2017, pp. 1152–1160.
   Google Scholar
• Karahanna, E.; Xu, S.X.; Xu, Y.; and Zhang, N. The needs-affordances-features perspective for the use of social media. MIS Quarterly, 42, 3 (2018), 737–756.
   Web of Science ®Google Scholar
• Koch, C.; Werner, S.; Rizk, A.; and Steinmetz, R. MIRA: Proactive music video caching using convnet-based classification and multivariate popularity prediction. In 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. 2018, pp. 109–115.
   Google Scholar
• Krijestorac, H.; Garg, R.; and Mahajan, V. Cross-platform spillover effects in consumption of viral content: A quasi-experimental analysis using synthetic controls. Information Systems Research, 31, 2 (2020), 449–472.
   Web of Science ®Google Scholar
• Laugel, T.; Lesot, M.-J.; Marsala, C.; Renard, X.; and Detyniecki, M. The dangers of post-hoc interpretability: Unjustified counterfactual explanations. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ( IJCAI-19). 2019, pp. 2801–2807.
   Google Scholar
• Lee, D.; Manzoor, E.; and Cheng, Z. Focused Concept Miner (FCM): Interpretable deep learning for text exploration. SSRN Electronic Journal, SSRN 3304756 (May 2018).
   Google Scholar
• Lee, J.W.; and Chan, Y.Y. Fine-grained plant identification using wide and deep learning model. In 2019 International Conference on Platform Technology and Service. 2019, pp. 1–5.
   Google Scholar
• Li, J.; Larsen, K.; and Abbasi, A. Theoryon: A design framework and system for unlocking behavioral knowledge through ontology learning. MIS Quarterly, 44, 4 (2020), 1733–772.
   Web of Science ®Google Scholar
• Lin, Y.K.; Chen, H.; Brown, R.A.; Li, S.H.; and Yang, H.J. Healthcare predictive analytics for risk profiling in chronic care. MIS Quarterly, 41, 2 (2017), 473–495.
   Web of Science ®Google Scholar
• Lin, Y.-K.; and Fang, X. First, do no harm: Predictive analytics to reduce in-hospital adverse events. Journal of Management Information Systems, 38, 4 (2021), 1122–1149.
   Web of Science ®Google Scholar
• Liu, X.; Zhang, B.; Susarla, A.; and Padman, R. Go to YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions. MIS Quarterly, 44, 1 (2020), 257–283.
   Web of Science ®Google Scholar
• Lundberg, S.M.; and Lee, S.-I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 2017. 2017.
   Google Scholar
• Mai, F.; Shan, Z.; Bai, Q.; Wang, X. (Shane); and Chiang, R.H.L. How does social media impact bitcoin value? A Test of the silent majority hypothesis. Journal of Management Information Systems, 35, 1 (2018), 19–52.
   Web of Science ®Google Scholar
• Miller, G.A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 2 (1956), 81–97.
   PubMed Web of Science ®Google Scholar
• Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, (2019), 1–38.
   Web of Science ®Google Scholar
• Molnar, C. Interpretable Machine Learning. Lulu. com, 2019.
   Google Scholar
• Montavon, G.; Binder, A.; Lapuschkin, S.; Samek, W.; and Müller, K.-R. Layer-wise relevance propagation: An overview. Lecture Notes in Computer Science, (2019), 193–209.
   Google Scholar
• Murdoch, W.J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; and Yu, B. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116, 44 (2019), 22071–22080.
   Google Scholar
• NYTimes. YouTube discloses percentage of views that go to videos that break its rules - The New York Times. 2021. Accessed 16 April 2023. https://www.nytimes.com/2021/04/06/technology/youtube-views.html.
   Google Scholar
• Rai, A. Editor’s comments: diversity of Design Science Research. MIS Quarterly, 41, 1 (2017), iii–xviii.
   Web of Science ®Google Scholar
• Ribeiro, M.T.; Singh, S.; and Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016), 1135–1144.
   Google Scholar
• Ruby, D. YouTube Statistics (2022) — Updated data, facts & figures shared! DEMANDSAGE, 2022. Accessed April 16, 2023. https://www.demandsage.com/youtube-stats/.
   Google Scholar
• Saboo, A.R. Using big data to model time-varying effects for marketing resource (RE) allocation. MIS Quarterly, 40, 4 (2016), 911–939.
   Web of Science ®Google Scholar
• Samtani, S.; Chai, Y., and Chen, H. Linking exploits from the dark web to known vulnerabilities for proactive cyber threat intelligence: An attention-based deep structured semantic model MIS Quarterly, 46, 2 (2021), 911–946.
   Web of Science ®Google Scholar
• Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D., and Batra, D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, (2017), 618–626.
   Google Scholar
• Shin, D.; He, S.; Lee, G.M.; Whinston, A.B.; Cetintas, S.; and Lee, K.-C. Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach. MIS Quarterly, 44, 4 (2020), 1459–1492.
   Web of Science ®Google Scholar
• Siegmund, N.; Kolesnikov, S.S.; Kästner, C.; Apel, S.; Batory, D.; Rosenmüller, M.; Saake, G. Predicting performance via automated feature-interaction detection. In International Conference on Software Engineering (ICSE). IEEE, 2012, pp. 167–177.
   Google Scholar
• Slack, D.; Hilgard, S.; Jia, E.; Singh, S.; and Lakkaraju, H. Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI Conference on AI, Ethics, and Society. 2020, pp. 180–186.
   Google Scholar
• Stieglitz, S.; and Dang-Xuan, L. Emotions and information diffusion in social media - Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29, 4 (2013), 217–248.
   Web of Science ®Google Scholar
• Tosun, N.; Sert, E.; Ayaz, E.; Yilmaz, E.; and Gol, M. Solar power generation analysis and forecasting real-world data using LSTM and autoregressive CNN. In In 2020 International Conference on Smart Energy Systems and Technologies. 2020, pp. 1–6.
   Google Scholar
• Tsang, M.; Cheng, D.; and Liu, Y. Detecting statistical interactions from neural network weights. In ICLR 2018. 2018.
   Google Scholar
• Vynck, G. de, and Lerman, R. Facebook and YouTube are still full of covid misinformation - The Washington Post. The Washington Post, 2021. Accessed April 16, 2023.https://www.washingtonpost.com/technology/2021/07/22/facebook-youtube-vaccine-misinformation/
   Google Scholar
• Xie, J.; Liu, X.; Zeng, D.; and Fang, X. Understanding reasons for medication nonadherence: An exploration in social media using sentiment-enriched deep learning approach. In ICIS 2017 Proceedings. 2017.
   Google Scholar
• Xie, J.; Liu, X.; Zeng, D.D.; and Fang, X. Understanding medication nonadherence from social media: A sentiment-enriched deep learning approach. MIS Quarterly, 46, 1 (2022), 341–372.
   Web of Science ®Google Scholar
• Xie, J.; and Zhang, B. Readmission risk prediction for patients with heterogeneous hazard: A trajectory-aware deep learning approach. In ICIS 2018 Proceedings. 2018.
   Google Scholar
• Xie, J.; Zhang, Z.; Liu, X.; and Zeng, D. Unveiling the hidden truth of drug addiction: A social media approach using similarity network-based deep learning. Journal of Management Information Systems, 38, 1 (2021), 166–195.
   Web of Science ®Google Scholar
• Xie, L.; Hu, Z.; Cai, X.; Zhang, W.; and Chen, J. Explainable recommendation based on knowledge graph and multi-objective optimization. Complex & Intelligent Systems 2021 7:3, 7, 3 (2021), 1241–1252.
   Web of Science ®Google Scholar
• Yang, M.; Ren, Y.; and Adomavicius, G. Understanding user-generated content and customer engagement on Facebook business pages. Information Systems Research, 30, 3 (2019), 839–855.
   Web of Science ®Google Scholar
• Ye, H.; Cao, B.; Peng, Z.; Chen, T.; Wen, Y.; and Liu, J. Web services classification based on wide & Bi-LSTM model. IEEE Access, 7, (2019), 43697–43706.
   Google Scholar
• Yu, H.; Xie, L.; and Sanner, S. The lifecyle of a Youtube video: Phases, content and popularity. In International AAAI Conference on Web and Social Media. 2015, pp. 533–542.
   Google Scholar
• Yu, S.; Chai, Y.; Chen, H.; Brown, R.A.; Sherman, S.J.; and Nunamaker, J.F. Fall detection with wearable sensors: A hierarchical attention-based convolutional neural network approach. Journal of Management Information Systems, 38, 4 (2021), 1095–1121.
   Web of Science ®Google Scholar
• Yu, S.; Chai, Y.; Chen, H.; Sherman, S.J.; and Brown, R.A. Wearable sensor-based chronic condition severity assessment: An adversarial attention-based deep multisource multitask learning approach. MIS Quarterly, Forthcoming, (2022).
   Web of Science ®Google Scholar
• Zafar, M.R.; and Khan, N. Deterministic local interpretable model-agnostic explanations for stable explainability. Machine Learning and Knowledge Extraction, 3, 3 (2021), 525–541.
   Google Scholar
• Zeiler, M.D.; and Fergus, R. Visualizing and understanding convolutional networks. Lecture Notes in Computer Science, (2014), 818–833.
   Google Scholar
• Zhang, D.; Zhou, L.; Kehoe, J.L.; and Kilic, I.Y. What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33, 2 (April 2016), 456–481.
   Web of Science ®Google Scholar
• Zhu, H.; Samtani, S.; Brown, R.A.; and Chen, H. A deep learning approach for recognizing activity of daily living (adl) for senior care: Exploiting interaction dependency and temporal patterns. MIS Quarterly, 45, 2 (2021), 859–896.
   Web of Science ®Google Scholar
• Zhu, H.; Samtani, S.; Chen, H.; and Nunamaker, J.F. Human identification for activities of daily living: A deep transfer learning approach. Journal of Management Information Systems, 37, 2 (2020), 457–483.
   Web of Science ®Google Scholar