We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. We validate our approach by implementing the methodology into an actual working system; we then evaluate it via quantitative methods by examining the accuracy of predicting content preference of personas, the stability of the personas over time, and the generalizability of the method via applying to two other datasets. Research findings show the approach can develop rich personas representing the behavior and demographics of real audiences using privacy-preserving aggregated online social media data from major online platforms. Results have implications for media companies and other organizations distributing content via online platforms.

中文翻译：

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代表实数的虚构人物

我们开发了一种方法，通过处理社交媒体受众的复杂行为和人口统计数据来自动创建想象中的人，称为角色。从一个受欢迎的社交媒体帐户中，来自 198 个国家/地区的观众参与了超过 3000 万次互动，并与一家全球媒体公司制作的 4,200 多个在线视频进行互动，我们证明我们的方法具有几项新颖的成就，包括：(a) 识别不同的用户行为基于用户内容消费模式的细分；(b) 确定有影响力的人口统计分组；(c) 通过自动添加相关属性（例如姓名、照片和个人特征）来创建丰富的角色描述。我们通过将方法实施到实际工作系统中来验证我们的方法；然后，我们通过定量方法对其进行评估，方法是检查预测角色内容偏好的准确性、角色随时间的稳定性以及该方法通过应用于其他两个数据集的普遍性。研究结果表明，该方法可以使用来自主要在线平台的隐私保护聚合在线社交媒体数据，开发代表真实受众行为和人口统计的丰富角色。结果对通过在线平台分发内容的媒体公司和其他组织有影响。研究结果表明，该方法可以使用来自主要在线平台的隐私保护聚合在线社交媒体数据，开发代表真实受众行为和人口统计的丰富角色。结果对通过在线平台分发内容的媒体公司和其他组织有影响。研究结果表明，该方法可以使用来自主要在线平台的隐私保护聚合在线社交媒体数据，开发代表真实受众行为和人口统计的丰富角色。结果对通过在线平台分发内容的媒体公司和其他组织有影响。