SocialML: machine learning for social media video creators

TL;DR

This paper presents machine learning tools developed for social media video creators, enabling content optimization, predictive analytics, and interactive support, resulting in increased viewer engagement and content quality.

Contribution

It introduces a suite of ML-powered tools for social media video creators, including content evaluation, thumbnail selection, and popularity prediction, tailored for real-world deployment.

Findings

Tools increased average video views by 12.9%

Enhanced content creation and evaluation processes

Successful deployment insights for social media analytics

Abstract

In the recent years, social media have become one of the main places where creative content is being published and consumed by billions of users. Contrary to traditional media, social media allow the publishers to receive almost instantaneous feedback regarding their creative work at an unprecedented scale. This is a perfect use case for machine learning methods that can use these massive amounts of data to provide content creators with inspirational ideas and constructive criticism of their work. In this work, we present a comprehensive overview of machine learning-empowered tools we developed for video creators at Group Nine Media - one of the major social media companies that creates short-form videos with over three billion views per month. Our main contribution is a set of tools that allow the creators to leverage massive amounts of data to improve their creation process, evaluate their videos before the publication and improve content quality. These applications include an interactive conversational bot that allows access to material archives, a Web-based application for automatic selection of optimal video thumbnail, as well as deep learning methods for optimizing headline and predicting video popularity. Our A/B tests show that deployment of our tools leads to significant increase of average video view count by 12.9%. Our additional contribution is a set of considerations collected during the deployment of those tools that can hel