Understanding Demographic Bias and Representation in Social Media Health Data

Nina L. Cesare, Christan Earl Grant, E. Nsoesie
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引用次数: 11

Abstract

Text, images, geotags and other data from social media sites lend researchers a unique window into population health trends and disease spread. While these data provide the opportunity to track and measure health outcomes across geographic regions, over extended periods of time, and through complex social networks, they also present challenges. Most notably, these data carry significant biases due to demographic differences in who chooses to use each platform, and what they choose to share. While several publications have discussed the limitations of leveraging social media data for public health research, the amount of literature systematically investigating their demographic bias and exploring mitigation strategies is limited and ripe for interdisciplinary contributions. In this discussion paper, we highlight that understanding the strengths and limitations of these data sources would enable a rigorous assessment of their usefulness for public health research and provide a means for quantifying uncertainty in research findings.
了解社会媒体健康数据中的人口统计学偏差和代表性
来自社交媒体网站的文本、图像、地理标签和其他数据为研究人员提供了一个了解人口健康趋势和疾病传播的独特窗口。虽然这些数据为跨越地理区域、在较长时间内并通过复杂的社会网络跟踪和衡量健康结果提供了机会,但它们也带来了挑战。最值得注意的是,由于选择使用每个平台的人群以及他们选择分享的内容的人口统计学差异,这些数据带有明显的偏见。虽然一些出版物讨论了利用社交媒体数据进行公共卫生研究的局限性,但系统调查其人口偏见和探索缓解策略的文献数量有限,适合跨学科贡献。在本讨论文件中,我们强调,了解这些数据来源的优势和局限性将有助于严格评估其对公共卫生研究的有用性,并为量化研究结果的不确定性提供一种手段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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