# Predicting Social Media Engagement from Emotional and Temporal Features

## TL;DR

This paper introduces a machine learning model that predicts social media engagement using emotional and temporal features, demonstrating high accuracy for likes but lower for comments, highlighting different influencing factors.

## Contribution

The study presents a novel approach combining emotional and temporal metadata with view counts to predict engagement, with a focus on modeling and evaluating these predictions.

## Key findings

- R^2 = 0.98 for likes prediction
- R^2 = 0.41 for comments prediction
- Likes are mainly driven by affective and exposure signals

## Abstract

We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets. Performance is evaluated with both a custom order of magnitude accuracy and standard regression metrics, including the coefficient of determination (R^2). Results show that emotional and temporal metadata, together with existing view counts, predict future engagement effectively. The model attains R^2 = 0.98 for likes but only R^2 = 0.41 for comments. This gap indicates that likes are largely driven by readily captured affective and exposure signals, whereas comments depend on additional factors not represented in the current feature set.