Instagram post popularity trend analysis and prediction using hashtag, image assessment, and user history features

Abstract

Instagram is one of the most popular social networks for marketing. Predicting the popularity of a post on Instagram is important to determine the influence of a user for marketing purposes. There were studies on popularity prediction on Instagram using various features and datasets. However, they haven't fully addressed the challenge of data variability of the global dataset, where they either used local datasets or discretized output. This research compared several regression techniques to predict the Engagement Rate (ER) of posts using a global dataset. The prediction model, coupled with the results of the popularity trend analysis, will have more utility for a larger audience compared to existing studies. The features were extracted from hashtags, image analysis, and user history. It was found that image quality, posting time, and type of image highly impact ER. The prediction accuracy reached up to 73.1% using the Support Vector Regression (SVR), which is higher than previous studies on a global dataset. User history features were useful in the prediction since the data showed a high variability of ER if compared to a local dataset. The added manual image assessment values were also among the top predictors.