SimTube: Generating Simulated Video Comments through Multimodal AI and User Personas
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916485308350464 |
|---|---|
| author | Hung, Yu-Kai Huang, Yun-Chien Su, Ting-Yu Lin, Yen-Ting Cheng, Lung-Pan Wang, Bryan Sun, Shao-Hua |
| author_facet | Hung, Yu-Kai Huang, Yun-Chien Su, Ting-Yu Lin, Yen-Ting Cheng, Lung-Pan Wang, Bryan Sun, Shao-Hua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimTube: Generating Simulated Video Comments through Multimodal AI and User Personas Hung, Yu-Kai Huang, Yun-Chien Su, Ting-Yu Lin, Yen-Ting Cheng, Lung-Pan Wang, Bryan Sun, Shao-Hua Human-Computer Interaction 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. |
| title | SimTube: Generating Simulated Video Comments through Multimodal AI and User Personas |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.09577 |