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How Long Will Your Videos Remain Popular? Empirical Study with Deep Learning and Survival Analysis

  • Received : 2022.07.12
  • Accepted : 2023.01.13
  • Published : 2023.06.30

Abstract

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos' popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos' tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos' long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A2A01064799)

References

  1. Arnold, M. J., and Reynolds, K. E. (2003). Hedonic Shopping Motivations. Journal of Retailing, 79, 77-95. https://doi.org/10.1016/S0022-4359(03)00007-1 
  2. Babin, B. J., Darden, W. R., and Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644-656. https://doi.org/10.1086/209376 
  3. Borges-Tiago, M. T., Tiago, F., and Cosme, C. (2018). Exploring users' motivations to participate in viral communication on social media. Journal of Business Research, (June), 1-9. https://doi.org/10.1016/j.jbusres.2018.11.011 
  4. Bothner, M. S., Kim, Y. K., and Lee, W. (2015). Primary status, complementary status, and organizational survival in the U.S. venture capital industry. Social Science Research, 52, 588-601. https://doi.org/10.1016/j.ssresea rch.2015.03.010 
  5. Bradley, G. T., and LaFleur, E. K. (2016). Toward the development of hedonic-utilitarian measures of retail service. Journal of Retailing and Consumer Services, 32, 60-66. https://doi.org/10.1016/j.jretconser.2016.06.001 
  6. Cisco, and Cisco Systems, I. (2019). Cisco visual networking index: Forecast and trends, 2017-2022 White Paper. Cisco Forecast and Methodology, 2017-2022. Retrieved from http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360_ns827_Networking_Solutions_White_Paper.html. 
  7. Clement, J. (2019). YouTube - statistics & facts. Statista, Jun 25, 2019. 
  8. Deng, Y., Loy, C. C., & Tang, X. (2017). Image aesthetic assessment: An experimental survey. IEEE Signal Processing Magazine, 34(4), 80-106. https://doi.org/10.1109/MSP.2017.2696576 
  9. Figueiredo, F., Benevenuto, F., and Almeida, J. M. (2011). The tube over time : characterizing popularity growth of YouTube videos categories and subject descriptors. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (pp. 745-754). WSDM 2011, Hong Kong, China. https://doi.org/10.1145/1935826.1935925 
  10. Fontanini, G., Bertini, M., and Del Bimbo, A. (2016). Web video popularity prediction using sentiment and content visual features. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval June 2016 (pp. 289-292). https://doi.org/10.1145/29 11996.2912053 
  11. Fu, W. W. (2012). Selecting online videos from graphics, text, and view counts: The moderation of popularity bandwagons. Journal of Computer-Mediated Communication, 18(1), 46-61. https://doi.org/10.1111/j.1083-6101.2012.01593.x 
  12. Hollebeek, L. D., and Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45, 27-41. https://doi.org/10.1016/j.intmar.2018.07.003 
  13. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). "MobileNets: efficient convolutional neural networks for mobile vision applications. Retrieved from http://arxiv.org/abs/1704.04861 
  14. Keyzer, F. De, Dens, N., and Pelsmacker, P. De. (2017). Don't be so emotional! How tone of voice and service type affect the relationship between message valence and consumer responses to WOM in social media. Online Information Review, 41(7), 905-920. https://doi.org/10.1108/OIR-08-2016-0219 
  15. Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of web sites. International Journal of Human-computer Studies, 60(3), 269-298. https://doi.org/10.1016/j.ijhcs.2003.09.002 
  16. Lin, H. C., Bruning, P. F., and Swarna, H. (2018). Using online opinion leaders to promote the hedonic and utilitarian value of products and services. Business Horizons, 61(3), 431-442. https://doi.org/10.1016/j.bushor.2018.01.010 
  17. Mayer, S., & Landwehr, J. R. (2018). Quantifying visual aesthetics based on processing fluency theory: Four algorithmic measures for antecedents of aesthetic preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399. https://doi.org/10.1037/aca0000187 
  18. Moshagen, M., & Thielsch, M. T. (2010). Facets of visual aesthetics. International Journal of Human-computer Studies, 68(10), 689-709. https://doi.org/10.1016/j.ijhcs.2010.05.006 
  19. Pennebaker, J. W., Booth, R. J., Boyd, R. L., and Francis, M. E. (2015). Linguistic Inquiry and Word Count: LIWC2015. Pennebaker Conglomerates, Inc 
  20. Poyry, E., Parvinen, P., and Malmivaara, T. (2013). Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage. Electronic Commerce Research and Applications, 12(4), 224-235. https://doi.org/10.1016/j.elerap.2013.01.003 
  21. Richier, C., Altman, E., Elazouzi, R., Altman, T., Linares, G., & Portilla, Y. (2014). Modelling view-count dynamics in youtube. arXiv preprint arXiv: 1404.2570. https://doi.org/10.48550/arXiv.1404.2570 
  22. Setyani, V., Zhu, Y.-Q., Hidayanto, A. N., Sandhyaduhita, P. I., and Hsiao, B. (2019). Exploring the psychological mechanisms from personalized advertisements to urge to buy impulsively on social media. International Journal of Information Management, 48(November), 96-107. https://doi.org/10.1016/j.ijinfomgt.2019.01.007 
  23. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., and Lee, K. C. (2017). Content complexity, similarity, and consistency in social media: A deep learning approach. SSRN Electronic Journal, 1-50. https://doi.org/10.2139/ssrn.2830377 
  24. Talebi, H., and Milanfar, P. (2018). NIMA: Neural Image Assessment. In IEEE Transactions on Image Processing, 27(8), 3998-4011. https://doi.org/10.1109/TIP.2018.2831899 
  25. Tractinsky, N., and Lowengart, O. (2007). Web-store aesthetics in e-retailing: A conceptual framework and some theoretical implications. Academy of Marketing Science Review, 11(1), 1-19. 
  26. Yoon, S. H., and Kim, H. W. (2019). What content and context factors lead to selection of a video clip? The heuristic route perspective. Electronic Commerce Research, 19(3), 603-627. https://doi.org/10.1007/s10660-019-09355-6 
  27. Yu, H., Xie, L., and Sanner, S. (2015). The lifecyle of a Youtube video: Phases, content and popularity. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 533-542. https://doi.org/10.1609/icwsm.v9i1.14609