Open Access iconOpen Access


Popularity Prediction of Social Media Post Using Tensor Factorization

Navdeep Bohra1,2, Vishal Bhatnagar3, Amit Choudhary4, Savita Ahlawat2, Dinesh Sheoran2, Ashish Kumari2,*

1 USICT, GGSIPU, New Delhi, 110058, India
2 Maharaja Surajmal Institute of Technology, New Delhi, 110058, India
3 NSUT, East Campus (Formerly AIACTR), New Delhi, 110031, India
4 Maharaja Surajmal Institute, New Delhi, 110058, India

* Corresponding Author: Ashish Kumari. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 205-221.


The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that is tensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation by choosing the top contents that were most like each other. The tensor is factorised with the application of the Adam optimization. It has been modified such that the bias is now included in the gradient function of A-PARAFAC, and the value of the bias is updated after each iteration. The prediction accuracy is improved by 32.25% with this strategy compared to other state of the art methods.


Cite This Article

N. Bohra, V. Bhatnagar, A. Choudhary, S. Ahlawat, D. Sheoran et al., "Popularity prediction of social media post using tensor factorization," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 205–221, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1035


  • 623


  • 0


Share Link