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Image Tweet Popularity Prediction with Convolutional Neural Network

  • Yihong ZhangEmail author
  • Adam Jatowt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Predicting popularity of a post in microblogging services such as Twitter is an important task beneficial for both publishers and regulators. Traditionally, the prediction is done through various manually designed features extracted from post and user contexts. In recent years, deep learning models such as convolutional neural networks (CNN) have shown significant effectiveness in image processing. In this paper, we make a novel investigation of the effectiveness of deep learning models in predicting image post popularity, with the raw image as the input. In contrast to previous works that use existing model trained for object detection, we trained a CNN model targeting directly at predicting popularity. We show that a dedicated CNN is more effective than networks trained for other purposes and is comparable to text-based predictors.

Keywords

Image popularity prediction Microblog Deep learning 

Notes

Acknowledgement

This research has been supported by JSPS KAKENHI grants (#17H01828, #18K19841) and by MIC/SCOPE (#171507010) grant.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Social Informatics, Graduate School of InformaticsKyoto UniversityKyotoJapan

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