等级制度
计算机科学
能见度
用户生成的内容
社会化媒体
内容(测量理论)
多级模型
职位(财务)
多样性(控制论)
订单(交换)
广告
计量经济学
业务
万维网
数学
人工智能
经济
机器学习
数学分析
物理
财务
光学
市场经济
作者
Michael Scholz,Joachim Schnurbus,Harry Haupt,Verena Dorner,Andrea Landherr,Florian Probst
标识
DOI:10.1016/j.dss.2018.07.001
摘要
User- and marketer-generated content items on social media platforms are supposed to have an impact on economic target variables, such as variables measuring consumers' purchase behavior. The position of each content item – and thus the impact on economic variables – changes with newly appearing items. We propose a hierarchy score to capture the dynamics of the content items on social media platforms. In order to mimic the reduced visibility of earlier content items, our hierarchy score computes the position of content items based on the number of text line equivalents of content items above a particular item. Employing the proposed hierarchy score in a dynamic regression framework for data of a large online store yields improved estimates and predictions compared to a variety of other models.
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