Supplementing Survey-Based Analyses of Group Vaccination Narratives and Behaviors Using Social Media

描述(由申请人提供)：在公共卫生危机中，高质量、实时的数据是必不可少的。然而，依赖随机数字拨号的传统调查方法昂贵，难以立即部署，而且无法对没有固定电话的难以接触到的人群进行抽样，例如年轻人(18-30岁)和少数族裔。相比之下，这些群体大量使用社交媒体。尤其是Twitter，它提供了一个丰富的数据来源，可以用最低的成本来试验假说，它的使用范围很广，而且非常迅速。然后，在进行更深入的研究之前，可以修改这些假设。社交媒体数据给公共卫生官员和研究人员带来了挑战，他们的目标是测试新的假设和政策。这些挑战与数据集的大小以及筛选和验证这些数据的难度有关。因此，我们将开发和测试一种创新的计算工具，以克服这些挑战。该工具将通过促进与健康叙述、态度和行为相关的社交媒体数据的实时数据收集和严格的量化分析，来补充传统的调查技术。我们将通过将现有调查数据与社交媒体数据进行比较来验证我们的工具，这些数据涉及18-30岁的成年人、成年非裔美国人和所有年龄段的非白人拉美裔美国人--这三个人口统计类别的社交媒体使用率最高，参与调查研究的比率较低，季节性流感疫苗接种率最低。因此，我们的工具将使理论构建成为可能。我们将测试来自健康传播文献的假设，特别是关于群体态度如何形成和变化的假设，通过现有的理论和概念模型对态度和集体叙述进行分类，并建立新的理论来捕捉新兴和以前未确定的概念。最后，我们使用一个网站vaccinetrends.org传播我们的结果和新技术，该网站向研究社区提供经过处理的社交媒体数据。我们的方法提供了廉价的、即时的访问这些群体的态度，超越了传统的时间、金钱和数据访问的限制。我们的方法是新颖的，因为它结合了社交媒体分析和经过验证的调查技术的优势。我们将利用两个互补的总体样本，代表不同的时间尺度和人口统计数据，以快速而严格的方式测试假设。此外，我们的社交媒体分析将利用新技术来推断人口统计信息和社会群体成员，从而能够提取主要叙述-与拒绝接种疫苗并最终与行为相关的态度和内容。此外，我们将开发可供整个社会科学、计算机和健康科学的研究人员采用的工具和技术。最后，我们利用了比以前工作中发现的更广泛的数据源，包括数十亿条推特消息和公共论坛信息，这些信息将使对疫苗拒绝理由的深入自动化内容分析成为可能。

DESCRIPTION (provided by applicant): High quality, real-time data is essential in public health crises. Yet, traditional survey methods that rely on random-digit-dialing are expensive, difficult to deploy instantly, and fail to sample hard-to-reach populations without landline telephones, such as young adults (18-30) and minorities. In contrast, these groups heavily use social media. Twitter, in particular, is widely available and immediate, providing a rich data source that can be used to pilot hypotheses at minimal cost. These hypotheses can then be modified prior to a more in-depth study. Social media data pose challenges for public health officials and researchers who aim to test new hypotheses and policies. These challenges are related to the size of the dataset and the difficulty filtering and validating these data. We will therefore develop and test an innovative computational tool that overcomes these challenges. This tool will supplement traditional survey techniques by facilitating real-time data gathering and rigorous quantitative analysis of social media data related to health narratives, attitudes, and behaviors. We will validate our tool by comparing existing survey data to social media data about influenza vaccination among adults 18-30, adult African Americans, and non-White Hispanics of all ages - three demographic categories with the highest rates of social media use, lower rates of participation in survey research, and lowest rates of seasonal flu vaccination. Thus, our tool will enable theory building. We will test hypotheses derived from the health communication literature, especially regarding how group attitudes form and change, categorize attitudes and collective narratives by existing theories and conceptual models, and build new theory to capture emerging and previously unidentified concepts. Finally, we disseminate our results and novel techniques using a website, vaccinetrends.org, that provides processed social media data to the research community. Our approach offers inexpensive, immediate access to the attitudes of these groups, transcending traditional constraints of time, money, and data access. Our approach is novel because it combines the strengths of social media analysis with those of validated survey techniques. We will draw upon two complementary population samples, representing different timescales and demographics, in order to test hypotheses in a manner that is rapid yet rigorous. In addition, our social media analysis will draw upon novel techniques to infer demographic information and social group membership, enabling the extraction of master narratives - attitudes and content that are associated with rationales for vaccine refusal and, ultimately, behavior. In addition, we will develop tools and techniques that can be adopted by researchers throughout the social, computer, and health sciences. Finally, we draw upon a much more extensive data source than has been found in previous work, including billions of Twitter messages and public forum information that will enable in-depth automated content analysis of vaccine refusal rationales.