Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.

YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos.

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.

YouTube Video Analytics for Patient Education: An Exploratory Clustering of Obstructive Sleep Apnea Videos.

With the growing popularity of content-sharing platforms, patients are increasingly using the Internet as a critical source of health information. As one of the most popular video-sharing sites, YouTube provides easy access to health information seekers, but it is difficult and time-consuming to identify and retrieve high-quality videos that may serve as engaging patient education materials. This paper reports on an exploratory analysis of 317 YouTube videos on Obstructive Sleep Apnea (OSA) to better understand some key features of the videos and the relationships between them to facilitate subsequent video classification and recommendation. Features intrinsic to a video, such as video duration, and extrinsic, such as the number of views, are analyzed using unsupervised clustering methods and the Sankey diagram to discover the relationship between the clusters and their significance across different clusters, providing promising insights for the assessment of video quality.