@inproceedings{c398cad7519b4b2fb11170a1f79c3ee9,
title = "YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos",
abstract = "The COVID-19 pandemic has highlighted the dire necessity to improve public health literacy for societal resilience. YouTube 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.",
keywords = "COVID-19 literacy, healthcare informatics, machine learning, natural language processing, Visual social media",
author = "Yawen Guo and Xiao Liu and Anjana Susarla and Rema Padman",
note = "Publisher Copyright: {\textcopyright} 2024 International Medical Informatics Association (IMIA) and IOS Press.; 19th World Congress on Medical and Health Informatics, MedInfo 2023 ; Conference date: 08-07-2023 Through 12-07-2023",
year = "2024",
month = jan,
day = "25",
doi = "10.3233/SHTI231067",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "760--764",
editor = "Jen Bichel-Findlay and Paula Otero and Philip Scott and Elaine Huesing",
booktitle = "MEDINFO 2023 - The Future is Accessible",
address = "Netherlands",
}