Popularity Prediction of Posts in Social Networks Based on User, Post and Image Features - Gayberi, Mehmetcan; Ögüdücü, Sule Gündüz (ACM, 2019)
This paper presents an approach to popularity prediction task. The approach differs from existing works by combining enriched user and post features with statistical features and image object detection related features. Moreover, in this paper, generic popularity prediction models are built that can make predictions for all types of posts from any users which is different from existing works. Briefly, the study contributes by combining various types of features, using more image related visual features and having a dramatically larger dataset compared to previous studies. A specific dataset containing 210.630 posts was crawled from Instagram in order to be used in the study and state-of-the-art Machine Learning algorithms were run on the dataset. Models predicted the log-normalized number of likes of posts as popularity value (ranging between 0 and 18.48) and the results show that the popularity of Instagram posts can be predicted with 0.92 rank-order correlation and 0.4212 Mean Absolute Error. The results indicate that combining user and post features with statistical features and image object detection related features yields good performance on popularity prediction.