Corpus ID: 236881532

Predicting Popularity of Images Over 30 Days

@article{Dutta2021PredictingPO,
  title={Predicting Popularity of Images Over 30 Days},
  author={Amartya Dutta and Ferdous Ahmed Barbhuiya},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.01326}
}
  • Amartya Dutta, F. Barbhuiya
  • Published 2021
  • Computer Science
  • ArXiv
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… Expand

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SHOWING 1-7 OF 7 REFERENCES
Prediction of Social Image Popularity Dynamics
TLDR
The proposed 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, which means that the prediction can be performed before posting the photo. Expand
Predicting Social Image Popularity Dynamics at Time Zero
TLDR
An approach in which the engagement score is formulated as a composition of two information associated to the evolution over time (shape) and the order of magnitude (scale) of the sequence is presented. Expand
What makes an image popular?
TLDR
The importance of image cues, such as color, gradients, deep learning features and the set of objects present, as well as the importance of various social cues such as number of friends or number of photos uploaded that lead to high or low popularity of images are shown. Expand
The Pulse of News in Social Media: Forecasting Popularity
TLDR
This paper constructs a multi-dimensional feature space derived from properties of an article and evaluates the efficacy of these features to serve as predictors of online popularity and demonstrates that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Expand
Beyond friendship graphs: a study of user interactions in Flickr
TLDR
This paper characterize indirect fan-owner interactions through photos among users in a large photo-sharing OSN, namely Flickr, and shows that a very small fraction of users in the main component of the friendship graph is responsible for the vast majority of fan- owner interactions. Expand
Learning Deep Features for Scene Recognition using Places Database
TLDR
A new scene-centric database called Places with over 7 million labeled pictures of scenes is introduced with new methods to compare the density and diversity of image datasets and it is shown that Places is as dense as other scene datasets and has more diversity. Expand
Large-scale visual senti