Predicting Popularity of Facebook Videos Through Visual Features Using Support Vector Machine Classifier

  • Bruna M. Dalmoro PUCRS
  • Soraia R. Musse PUCRS

Resumo


With the popularization of social networks, the sharing and consumption of content in video format becomes easier. Understanding what makes a video popular and being able to predict its popularity in number of views is useful for both content creators and advertising. In this work, we explore visual features extracted from 1,820 Facebook videos in order to predict whether they will reach more than a certain number of views on the seven days after publication. For this purpose, we used Support Vector Machine with Gaussian Radial Basis Function classification model. Using only visual features as predictors, the model with Video Characteristics and Rigidity features combined reached Kappa of 0.7324, sensitivity of 0.8930, and positive predictive value of 0.8930.
Palavras-chave: Video Popularity, Visual Features, SVM classifier

Referências

Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pages 144–152. ACM Press.

Facebook for Business (2020). In-stream ads. Online; accessed 06-Apr-2020.

Goodall, C. R. (1993). 13 computation using the qr decomposition. In Computational Statistics, volume 9 of Handbook of Statistics, pages 467–508. Elsevier.

Khosla, A., Das Sarma, A., and Hamid, R. (2014). What makes an image popular? In Proceedings of the 23rd international conference on World wide web, pages 867–876. ACM.

Kong, Q., Rizoiu, M.-A., Wu, S., and Xie, L. (2018). Will this video go viral: Explaining and predicting the popularity of youtube videos. In Companion Proceedings of the The Web Conference 2018, pages 175–178. International World Wide Web Conferences Steering Committee.

Kuhn, M. et al. (2008). Building predictive models in r using the caret package. Journal of statistical software, 28(5):1–26.

Kuhn, M. et al. (2019). caret: Classification and Regression Training. R package version 6.0-84.

Kuhn, M. and Johnson, K. (2013). Applied predictive modeling, volume 26. Springer.

Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.

R Core Team (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1):72–101

Trzciński, T. and Rokita, P. (2017). Predicting popularity of online videos using supportvector regression. IEEE Transactions on Multimedia, 19(11):2561–2570.

Vieira, S. M., Kaymak, U., and Sousa, J. M. C. (2010). Cohen’s kappa coefficient as a performance measure for feature selection. In International Conference on Fuzzy Systems, pages 1–8

Youtube About (2020). Press. Online; accessed 06-Apr-2020.

YouTube Support (2019). How to earn money on youtube. Online; accessed 06-Apr-2020.
Publicado
18/07/2021
DALMORO, Bruna M.; MUSSE, Soraia R.. Predicting Popularity of Facebook Videos Through Visual Features Using Support Vector Machine Classifier. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 131-138. ISSN 2595-6205. DOI: https://doi.org/10.5753/semish.2021.15815.