Machine learning models to predict the engagement level of Twitter posts: Indonesian e-commerce case study

Abstract

The growing utilization of social media platforms enables direct interaction between companies and consumers. However, the expanding range of interactions and real-world data complexities necessitate the development of more sophisticated decision models. To address this, the current research focuses on constructing machine learning models, namely multinomial logistic regression, decision tree, k-nearest neighbor, and random forest, to forecast the engagement level of Twitter posts from three prominent e-commerce platforms in Indonesia: Bukalapak, Blibli, and Tokopedia. The analysis comprises a dataset of 12,786 unique tweets, accumulating 11,870,254 favorites and 2,735,886 retweets over a seven-month period from February 1 to August 31, 2021. The prediction models are built upon three theoretical constructs with seven features, encompassing interactivity (e.g., links, hashtags), vividness (e.g., images, short videos, long videos), and temporal factors (e.g., day of post, last post time). Factors such as post frequency, interactive posting elements, and static visual elements emerge as significant features for predicting the engagement level of Twitter posts. Results demonstrate that the random forest model outperforms singular classifier models, including multinomial logistic regression, decision tree, and k-nearest neighbor models, in terms of precision, recall, and F1 score.