| DC Field | Value | Language |
|---|
| dc.contributor.author | Louis Wong | - |
| dc.contributor.author | Ahmed Salih | - |
| dc.contributor.author | Mingyao Song | - |
| dc.contributor.author | Jason Xu | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-16T07:47:40Z | - |
| dc.date.available | 2024-02-16T07:47:40Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Multimodal Deep Regression on TikTok Content Success / Louis Wong [et al.] // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 35–41. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54286 | - |
| dc.description.abstract | Content creators grapple with the challenge of predicting if their investments will lead to increased viewership
and audience growth on social media platforms. By employing advanced techniques in video encoding and natural language processing, we construct a powerful multimodal ensemble model for accurately predicting video success. Our preliminary results demonstrate the model’s effectiveness in predicting video virality. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | virality | en_US |
| dc.subject | multimodal | en_US |
| dc.subject | predicting | en_US |
| dc.title | Multimodal Deep Regression on TikTok Content Success | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
|
