YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910593101856768 |
|---|---|
| author | Guo, Yawen Liu, Xiao Susarla, Anjana Padman, Rema |
| author_facet | Guo, Yawen Liu, Xiao Susarla, Anjana Padman, Rema |
| contents | The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube, the largest video-sharing social media platform, provides a vast repository of user-generated health information in a multi-media-rich format which may be easier for the public to understand and use if major concerns about content quality and accuracy are addressed. This study develops an automated solution to identify, retrieve and shortlist medically relevant and understandable YouTube videos that domain experts can subsequently review and recommend for disseminating and educating the public on the COVID-19 pandemic and similar public health outbreaks. Our approach leverages domain knowledge from human experts and machine learning and natural language processing methods to provide a scalable, replicable, and generalizable approach that can also be applied to enhance the management of many health conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09425 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos Guo, Yawen Liu, Xiao Susarla, Anjana Padman, Rema Information Retrieval The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube, the largest video-sharing social media platform, provides a vast repository of user-generated health information in a multi-media-rich format which may be easier for the public to understand and use if major concerns about content quality and accuracy are addressed. This study develops an automated solution to identify, retrieve and shortlist medically relevant and understandable YouTube videos that domain experts can subsequently review and recommend for disseminating and educating the public on the COVID-19 pandemic and similar public health outbreaks. Our approach leverages domain knowledge from human experts and machine learning and natural language processing methods to provide a scalable, replicable, and generalizable approach that can also be applied to enhance the management of many health conditions. |
| title | YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2312.09425 |