SimTube: Generating Simulated Video Comments through Multimodal AI and User Personas

TL;DR

SimTube is a multimodal AI system that generates realistic, diverse, and contextually relevant simulated video comments based on video content and user personas, aiding creators in content refinement prior to publication.

Contribution

The paper introduces SimTube, a novel system combining multimodal data and user personas to generate pre-release audience feedback, enhancing content creation processes.

Findings

Generated comments are relevant, believable, and diverse.

SimTube's comments are often more detailed than actual audience feedback.

System is effective across various video genres and audience demographics.

Abstract

Audience feedback is crucial for refining video content, yet it typically comes after publication, limiting creators' ability to make timely adjustments. To bridge this gap, we introduce SimTube, a generative AI system designed to simulate audience feedback in the form of video comments before a video's release. SimTube features a computational pipeline that integrates multimodal data from the video-such as visuals, audio, and metadata-with user personas derived from a broad and diverse corpus of audience demographics, generating varied and contextually relevant feedback. Furthermore, the system's UI allows creators to explore and customize the simulated comments. Through a comprehensive evaluation-comprising quantitative analysis, crowd-sourced assessments, and qualitative user studies-we show that SimTube's generated comments are not only relevant, believable, and diverse but often more detailed and informative than actual audience comments, highlighting its potential to help creators refine their content before release.