Michigan State University, East Lansing, MI, USA.
Arizona State University, Tempe, AZ, USA.
J Biomed Inform. 2024 Sep;157:104669. doi: 10.1016/j.jbi.2024.104669. Epub 2024 Jun 15.
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.
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.
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.
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.
研究证实，在线推荐平台存在显著的偏见，加剧了先前存在的差距，导致代表性不足的人群的结果不理想。我们研究了在 YouTube 社交媒体平台上传播的医疗保健信息中，纳入和代表性方面的偏见问题，该平台是一个广泛使用的多媒体丰富信息的在线渠道。美国有三分之一的成年人使用互联网了解健康问题，因此，评估互联网平台（如 YouTube）传播健康信息的包容性和代表性至关重要。
利用公平机器学习 (ML)、自然语言处理以及语音和面部识别方法，我们使用从 YouTube 平台提取的大量视频及其关于慢性疾病（糖尿病）的元数据，检查视频内容呈现者的包容性和代表性。回归模型用于确定演示者的人口统计学特征是否会影响视频的受欢迎程度，这是通过视频的平均日观看次数来衡量的。生成更高观看次数的视频被认为更受欢迎。
语音和面部识别方法成功地预测了演示者的性别和种族。性别通过语音识别预测（准确率= 78%，AUC= 76%），而性别和种族预测则使用面部识别（准确率= 93%，AUC= 92%和准确率= 82%，AUC= 80%）。只有当演示者的面部不可见时，演示者的性别才会对视频观看次数产生影响，而面部不可见的男性演示者的视频与观看次数呈正相关。此外，白人男性演示者的视频对观看次数有积极影响，而女性和非白人组的视频则有较高的观看次数。
如用于评估视频内容的纳入和代表性的先进语音和面部识别算法所示，演示者的人口统计学特征确实会影响在社交媒体平台上观看的视频的平均日观看次数。未来的研究可以探索短视频和频道级别，因为频道名称的知名度和与之相关的视频数量确实会对观看次数产生影响。
