Image Popularity Prediction Challenge



  • Sebastiano Battiato, Università degli Studi di Catania, Italy
  • Giovanni Maria Farinella, Università degli Studi di Catania, Italy
  • Alessandro Ortis, Università degli Studi di Catania, Italy


In the context of social media analysis, there are several applications that could benefit from the assessment and the prediction of the level of engagement achieved by a post shared by a user on a social platform (e.g., social media marketing, brand monitoring, and political parties’ popularity, etc.).

So far, researcher mostly focused on predicting the engagement score of an image measured at a precise instant of time. However, the popularity is a dynamic parameter. As consequence, two images with the same popularity dynamic could be ranked differently, depending on the time of analysis (i.e., download time).

This challenge is meant to develop systems able to predict the popularity of social images over time (i.e., popularity dynamics), given the information of the image post available only at posting time.

The main contributions of the task proposed for this challenge are the following:

  • it poses new questions around on-line behaviour, popularity, and social media content lifecycle;
  • it addresses a very challenging task which finds several practical uses in the context of social media analysis applications such as recommender systems and advertisement campaigns analysis/placement;
  • the developed systems will allow the definition of applications to support the publication and effective diffusion of contents through social media, by implementing a forecast of the engagement evolution over time. As instance, such a system can indicate when old contents should be replaced by new ones before they become obsolete.