Popularity Determinants of YouTube Videos

Authors

Keywords:

YouTube, user-generated videos, video sharing, popularity, quantile regression

Abstract

Este artigo estima os determinantes da popularidade dos vídeos no YouTube. Do modelo dinâmico teórico, o número de visualizações de um vídeo depende positivamente da categoria do vídeo, do número de vídeos por canal, do total de dias online e da quantidade de curtidas; é impactado negativamente pela duração do vídeo, pelo número de inscritos e pela quantidade de comentários. Os resultados empíricos revelam que os fatores de popularidade mais importantes são o número de vídeos por canal e o total de dias online. Os testes de robustez são realizados através de regressões quantílicas. O público pode variar entre categorias de conteúdo em termos de visualizações e respostas.

Author Biography

Claudio D. Shikida, Ibmec BH

Dr. Economia UFRGS

References

Baumeister R.F., Bratslavsky E., Finkenauer C., &Vohs, K.D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323-370.

Bi, N.C., Zhang, R. & Ha, L. (2019). Does the valence of product review matter?: The mediating role of self-effect and third-person effect in sharing YouTube word-of-mouth (vWOM). Journal of Interactive Marketing, 13(1), 79-95.

Borghol, Y., et al (2011). Characterizing and modelling popularity of user-generated videos. Performance Evaluation 68, 11, 1037-1055.

Brodersen, A., Scellato, S., Wattenhofer, M. (2012). YouTube around the world: Geographic popularity of videos. Proceedings of the Twenty-first International Conference on World Wide Web. 241-250.

Castañón, J. (2017). Análise de Popularidade de Canais do YouTube.UFOP/ICEA/DECSI, 2010.

Cha, M. et al. (2007). I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. Proceedings of the Seventh ACM Internet Measurement Conference,1-14.

Chatzopoulou, G., Sheng, C., Faloutsos, M. (2010). A first step towards understanding popularity in YouTube. IEEE INFOCOM 2010 Proceedings.

Chen, C. (2013). Exploring personal branding on YouTube. Journal of Internet Commerce, 12(4), 332-347.

Cheng, X., Dale, C., & Lui, J. (2008). Statistics and social network of YouTube videos. IWQoS, 229-238.

Figueiredo, F. Benevenuto, F., & Almeida, J.M. (2011). The tube over time: Characterizing popularity growth of YouTube videos. Proceedings of Fourth ACM International Conference of Web Search and Data Mining.745-754.

Gronroos, C., & Voima, P. (2013). Critical service logic: making sense of value creation and co-creation. Journal of the Academy of Marketing Science, 41(2), 133–150.

Hair, M. & Ozcan, T. (2018). How reviewers’ use of profanity affects perceived usefulness of online reviews. Marketing Letters, 29(2), 151-163.

Hu, N., Pavlou, P.A., Zhang, J. (2009). Why do online product reviews have a j-shaped distribution? Overcoming biases in online word-of-the-mouth communication. Communications of the ACM, 52, 144-147.

Kamiyama, N., & Murata, M. (2019). Reproducing popularity distribution of YouTube videos. IEEE Transactions on Network and Service Management, 16(3), 1100-1112.

Kuppelwieser, V.G., Simpson, M.C., & Chiummo, G. (2013). 1+1 Does not always equal value creation: The case of YouTube. Marketing Letters, 24(3), 311-321.

Lamberton, C. & Stephen, A.T. (2016). A thematic exploration of digital, social Media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(6), 146-172.

Liu, B. & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In: Mining text data. Springer, Boston, MA, 415-463.

Mangold, W.G., Miller, F., Brockway, G.R. (1999). Word-of-mouth communication in the service marketplace. Journal of Services Marketing, 13(1), 73-89.

Moller, S. (2008). Customer integration—a key to an implementation perspective of service provision. Journal of Service Research, 11(2), 197-210.

Pak, A. & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. LREc. 1320-1326.

Pinto, H., Almeida, J. & Gonçalves, M. (2013). Using early view patterns to predict the popularity of YouTube videos. WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining, 365-374.

Rosario, A. B., Sotgiu, F., Valck, K. D., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53, 297–31

Schwenner, C. & Ziewiecki, S. (2018). Social media sellout: The increasing role of product promotion on YouTube. Social Media + Society, 4(3), 1-20.

Spangler, T. (2020, April 28). YouTube Q1 revenue climbs 33% to $4 billion, parent alphabet saw big ad sales slowdown in March. Variety. https://variety.com/2020/digital/news/youtube-q1-2020-revenue-4-billion-alphabet-misses-1234592075/

Szabo, G. & Huberman, B. (2010). Predicting the popularity of on-line content. Communications of the ACM, 53(8).

The Diffusion Group (TDG): The Future of TV: A view from 2013. https://www.tdgresearch.com/what-we-do/syndicated-reports/the-future-of-tv-a-view-from-2013/. Accessed 31 March 2018

The Diffusion Group (TDG): The Future of TV Viewing, 2016-2025. https://www.tdgresearch.com/what-we-do/syndicated-reports/the-future-of-tv-viewing-2016-2025/. Accessed 31 March 2018

Thoumrungroje, A. (2014). The Influence of social intensity and EWOM on conspicuous consumption. Procedia - Social and Behavioral Sciences,148, 7-15.

Top 250 YouTubers Channels in Brazil. https://socialblade.com/youtube/top/country/br. Accessed 17 Sept. 2018.

van Noort, G., M.L. Antheunis, E.A. van Reijmersdal (2012). "Social Connections and the Persuasiveness of Viral Campaigns in Social Network Sites: Persuasive Intent as the Underlying Mechanism," Journal of Marketing Communications, 18(1), 39-53.

Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1-17.

Vargo, S. L., & Lusch, R. F. (2008a). Service-dominant logic: continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1-10.

Vargo, S. L., & Lusch, R. F. (2008b). Why “service”? Journal of the Academy of Marketing Science, 36(1), 25-38.

White, H. (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrics, 48, 817-838.

Wu, S., Rizoiu, M., Xie, L. (2018). Beyond views: Measuring and predicting engagement in on-line videos. https://arxiv.org/pdf/1709.02541.pdf

Yin, D., Bond, S., Zhang, H. (2013). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38, 539-560.

Yoganarasimhan, H. (2012). Impact of social network structure on content propagation: A study using YouTube data. Quantitative Marketing Economics, 10, 111-150.

Zhou, R., Khemmarat, S., Gao, L., Wan, J., Zhang, J. (2016). How YouTube videos are discovered and its impact on video views. Multimedia Tools and Applications, 75(10). https://doi.org/10.1007/s11042-015-3206-0

Zink, M., Gu, Y., Kyoungwon, S., Kurose, J. (2009). Characteristics of YouTube network traffic at a campus network – Measurements, models, and implications. Computer Networks, 53(4), 501-514.

Zwass, V. (2010). Co-creation: toward a taxonomy and an integrated research perspective. International Journal of Electronic Commerce, 15(1), 11-48.

Downloads

Published

2026-06-17

How to Cite

Araujo Junior, A. F., & Shikida, C. D. (2026). Popularity Determinants of YouTube Videos. Revista Gestão & Tecnologia, 26(1), 73–93. Retrieved from https://revistagt.fpl.emnuvens.com.br/get/article/view/3031

Issue

Vol. 26 No. 1 (2026)

Section

ARTIGO

License

Copyright (c) 2026 Revista Gestão & Tecnologia

Creative Commons License

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

Authors who publish with this journal agree to the following terms: • 1. The author(s) authorize the publication of the article in the journal. • 2. The author(s) ensure that the contribution is original and unpublished and is not being evaluated in other journal(s). • 3. The journal is not responsible for the opinions, ideas and concepts expressed in the texts because they are the sole responsibility of the author(s). • 4. The publishers reserve the right to make adjustments and textual adaptation to the norms of APA. • 5. Authors retain copyright and grant the journal right of first publication, with the work [SPECIFY PERIOD OF TIME] after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. • 6. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. • 7. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access) at http://opcit.eprints.org/oacitation-biblio.html