Predicting Popularity of Images Over 30 Days

TL;DR

This paper proposes a method to predict the 30-day popularity of Flickr images using social and visual features, aiming to forecast engagement before upload.

Contribution

It introduces a novel approach that models image popularity based on predicted shape and scale, extending prior work with new insights and improvements.

Findings

Predicts 30-day engagement scores with reasonable accuracy

Utilizes social and visual features for popularity prediction

Builds on previous models with suggested enhancements

Abstract

The current work deals with the problem of attempting to predict the popularity of images before even being uploaded. This method is specifically focused on Flickr images. Social features of each image as well as that of the user who had uploaded it, have been recorded. The dataset also includes the engagement score of each image which is the ground truth value of the views obtained by each image over a period of 30 days. The work aims to predict the popularity of images on Flickr over a period of 30 days using the social features of the user and the image, as well as the visual features of the images. The method states that the engagement sequence of an image can be said to depend on two independent quantities, namely scale and shape of an image. Once the shape and scale of an image have been predicted, combining them the predicted sequence of an image over 30 days is obtained. The current work follows a previous work done in the same direction, with certain speculations and suggestions of improvement.