Davis Matthew A, Zheng Kai, Liu Yang, Levy Helen
University of Michigan School of Nursing, Ann Arbor, MI, United States.
Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, United States.
J Med Internet Res. 2017 May 26;19(5):e167. doi: 10.2196/jmir.6946.
The Affordable Care Act (ACA), often called "Obamacare," is a controversial law that has been implemented gradually since its enactment in 2010. Polls have consistently shown that public opinion of the ACA is quite negative.
The aim of our study was to examine the extent to which Twitter data can be used to measure public opinion of the ACA over time.
We prospectively collected a 10% random sample of daily tweets (approximately 52 million since July 2011) using Twitter's streaming application programming interface (API) from July 10, 2011 to July 31, 2015. Using a list of key terms and ACA-specific hashtags, we identified tweets about the ACA and examined the overall volume of tweets about the ACA in relation to key ACA events. We applied standard text sentiment analysis to assign each ACA tweet a measure of positivity or negativity and compared overall sentiment from Twitter with results from the Kaiser Family Foundation health tracking poll.
Public opinion on Twitter (measured via sentiment analysis) was slightly more favorable than public opinion measured by the Kaiser poll (approximately 50% vs 40%, respectively) but trends over time in both favorable and unfavorable views were similar in both sources. The Twitter-based measures of opinion as well as the Kaiser poll changed very little over time: correlation coefficients for favorable and unfavorable public opinion were .43 and .37, respectively. However, we found substantial spikes in the volume of ACA-related tweets in response to key events in the law's implementation, such as the first open enrollment period in October 2013 and the Supreme Court decision in June 2012.
Twitter may be useful for tracking public opinion of health care reform as it appears to be comparable with conventional polling results. Moreover, in contrast with conventional polling, the overall amount of tweets also provides a potential indication of public interest of a particular issue at any point in time.
《平价医疗法案》（ACA），通常被称为“奥巴马医改”，是一项自2010年颁布以来逐步实施的备受争议的法律。民意调查一直显示，公众对ACA的看法相当负面。
我们研究的目的是考察推特数据可用于衡量公众对ACA随时间变化的看法的程度。
我们从2011年7月10日至2015年7月31日，使用推特的流式应用程序编程接口（API）前瞻性地收集了每日推文的10%随机样本（自2011年7月以来约5200万条）。使用一份关键词列表和特定于ACA的主题标签，我们识别出关于ACA的推文，并考察与ACA关键事件相关的关于ACA推文总量。我们应用标准文本情感分析为每条关于ACA的推文赋予一个积极或消极的度量，并将推特上的总体情感与凯撒家庭基金会健康追踪民意调查的结果进行比较。
推特上的公众意见（通过情感分析衡量）比凯撒民意调查衡量的公众意见略更积极（分别约为50%对40%），但两个来源中积极和消极观点随时间的趋势相似。基于推特的意见度量以及凯撒民意调查随时间变化很小：积极和消极公众意见的相关系数分别为0.43和0.37。然而，我们发现，针对该法律实施中的关键事件，如2013年10月的首个开放注册期和2012年6月的最高法院裁决，与ACA相关的推文数量大幅飙升。
推特可能有助于追踪医疗保健改革的公众意见，因为它似乎与传统民意调查结果相当。此外，与传统民意调查相比，推文的总量还在任何时间点提供了公众对特定问题兴趣的潜在指示。
