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Prediction of Social Image Popularity Dynamics

  • Alessandro OrtisEmail author
  • Giovanni Maria Farinella
  • Sebastiano Battiato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11752)

Abstract

This paper introduces the new challenge of forecasting the engagement score reached by social images over time. The task to be addressed is hence the estimation, in advance, of the engagement score dynamic over a period of time (e.g., 30 days) by exploiting visual and social features. To this aim, we propose a benchmark dataset that consists of \(\sim \)20K Flickr images labelled with their engagement scores (i.e., views, comments and favorites) in a period of 30 days from the upload in the social platform. For each image, the dataset also includes user’s and photo’s social features that have been proven to have an influence on the image popularity on Flickr. We also present a method to address the aforementioned problem. The engagement score dynamic is represented as the combination of two properties related to the dynamic and the magnitude of the engagement sequence, referred as shape and scale respectively. The proposed approach models the problem as the combination of two prediction tasks, which are addressed individually. Then, the two outputs are properly combined to obtain the prediction of the whole engagement sequence. Our approach is able to forecast the daily number of views reached by a photo posted on Flickr for a period of 30 days, by exploiting features extracted from the post. This means that the prediction can be performed before posting the photo.

Keywords

Image popularity prediction Social media engagement 

References

  1. 1.
    Almgren, K., Lee, J., et al.: Predicting the future popularity of images on social networks. In: Proceedings of the 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016, p. 15. ACM (2016)Google Scholar
  2. 2.
    Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity. In: ICWSM, vol. 12, pp. 26–33 (2012)Google Scholar
  3. 3.
    Battiato, S., et al.: Organizing videos streams for clustering and estimation of popular scenes. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 51–61. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68560-1_5CrossRefGoogle Scholar
  4. 4.
    Battiato, S., et al.: The social picture. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 397–400. ACM (2016)Google Scholar
  5. 5.
    Borth, D., Ji, R., Chen, T., Breuel, T., Chang, S.F.: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 223–232. ACM (2013)Google Scholar
  6. 6.
    Cappallo, S., Mensink, T., Snoek, C.G.: Latent factors of visual popularity prediction. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 195–202. ACM (2015)Google Scholar
  7. 7.
    Khosla, A., Das Sarma, A., Hamid, R.: What makes an image popular? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 867–876. ACM (2014)Google Scholar
  8. 8.
    Ortis, A., Farinella, G.M., Torrisi, G., Battiato, S.: Visual sentiment analysis based on objective text description of images. In: 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–6. IEEE (2018)Google Scholar
  9. 9.
    Ortis, A., Farinella, G.M., D’Amico, V., Addesso, L., Torrisi, G., Battiato, S.: RECfusion: automatic video curation driven by visual content popularity. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1179–1182. ACM (2015)Google Scholar
  10. 10.
    Overgoor, G., Mazloom, M., Worring, M., Rietveld, R., van Dolen, W.: A spatio-temporal category representation for brand popularity prediction. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 233–241. ACM (2017)Google Scholar
  11. 11.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  12. 12.
    Valafar, M., Rejaie, R., Willinger, W.: Beyond friendship graphs: a study of user interactions in Flickr. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 25–30. ACM (2009)Google Scholar
  13. 13.
    Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessandro Ortis
    • 1
    Email author
  • Giovanni Maria Farinella
    • 1
  • Sebastiano Battiato
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

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