Vaccine Images on Twitter: Analysis of What Images are Shared.
Vaccine Images on Twitter: Analysis of What Images are Shared.
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DOI：
10.2196/jmir.8221
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发表时间：
2018-04-03
影响因子：
7.4
通讯作者：
Dredze M
中科院分区：
文献类型：
--
作者：
Chen T;Dredze M
视觉图像在健康传播中起着关键作用;然而，人们对疫苗相关图像的哪些方面使其成为有效的传播辅助工具知之甚少。Twitter是一个流行的与疫苗接种相关的讨论场所，它提供了许多与推文共享的图像。这项研究的目的是了解如何在疫苗相关的推文中使用图像，并就疫苗相关图像的特征提供指导，这些特征与被转发的可能性较高相关。我们从Twitter上收集了超过100万条疫苗图像信息，并使用自动图像分析来表征这些图像的各种属性。我们拟合了一个逻辑回归模型来预测疫苗图像推文是否被转发，从而识别出与更高的共享可能性相关的特征。为了进行比较，我们为Facebook上的疫苗新闻分享和一般的图片推文建立了类似的模型。大多数与疫苗相关的图片都是重复的（125，916/237，478; 53.02%）或来自其他来源，不一定是由推文作者创建的。几乎一半的图像包含嵌入式文本，许多图像包含人和注射器的图像。视觉内容与推文的文本主题高度相关。疫苗图片推文的分享率是非图片推文的两倍。图像的情感和图像中显示的对象是确定图像是否被转发的预测因素。我们是第一个在Twitter上研究疫苗图片的人。我们的研究结果为疫苗图像的研究和使用提出了未来的方向，并可能为疫苗接种的传播策略提供信息。此外，我们的研究证明了图像分析的有效研究方法。
Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet’s textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis.
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