The significant emergence of the "popularity" phenomena has been fueled by the quick rise of influential social media platforms like Facebook and YouTube as well as the pervasive integration of electronic gadgets into daily life. This popularity essence essentially entails the rapid accrual of substantial views, frequently reaching into the thousands or millions, across videos, posts, and various content types, serving as a tangible reflection of user inclinations. The task of predicting content popularity is a formidable one due to its reliance on an array of factors, encompassing visual and social attributes such as views, likes, comments, as well as variables like publication time, publisher identity, duration, and content specifics. This manuscript presents a comprehensive exploration of this subject matter, delving into recent applications of machine learning techniques for the prediction of content popularity. It underscores the significance of judiciously selecting predictive attributes and appropriately configuring data models to attain accurate prognostications. The research work encompasses an array of regression models harnessed in machine learning, including decision trees, random forests, support vector machines, ridge regression, and both linear and non-linear regression. Diverse classes of attributes employed for popularity prediction are delineated, encompassing text-based features, visual characteristics, metadata with a social dimension, and the fusion of multiple attributes. The paper further outlines the prevalent assessment metrics employed for evaluating regression models, encompassing mean absolute error, mean squared error, and root mean squared error. Also, it includes a table that summarizes the references, models, content types, features, and results of various studies related to popularity prediction.
