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A Novel Multimodal Online News Popularity Prediction Model based on Ensemble Learning
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  • Vikas Hassija,
  • Anuja Arora,
  • Shivam Bansal,
  • Siddharth Yadav,
  • Vinay Chamola,
  • Amir Hussain
Vikas Hassija
National University of Singapore School of Computing

Corresponding Author:v.hasija@nus.edu.sg

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Anuja Arora
Jaypee Institute of Information Technology
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Shivam Bansal
National University of Singapore Department of Computer Science
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Siddharth Yadav
The NorthCap University
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Vinay Chamola
Birla Institute of Technology & Science Pilani
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Amir Hussain
Edinburgh Napier University School of Computing
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The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this paper, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this paper, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection, and effective modeling to create a robust news popularity Prediction Model. Three kinds of features – meta features, text features, and image features are used to design an influential and robust model. The performance measure Root Mean Squared logarithmic error (RMSLE) is used to validate the outcome of the proposed model. Further, the most important features are sought out for the proposed model to verify the dependence of the model on text and image features.
24 Feb 2023Submitted to Expert Systems
24 Feb 2023Submission Checks Completed
24 Feb 2023Assigned to Editor
06 Mar 2023Reviewer(s) Assigned
27 Mar 2023Review(s) Completed, Editorial Evaluation Pending
04 Apr 2023Editorial Decision: Revise Minor
19 Apr 20231st Revision Received
26 Apr 2023Submission Checks Completed
26 Apr 2023Assigned to Editor
26 Apr 2023Reviewer(s) Assigned
27 Apr 2023Review(s) Completed, Editorial Evaluation Pending
03 May 2023Editorial Decision: Accept