Broniatowski David A, Reyna Valerie F
The George Washington University.
Cornell University.
Comput Math Organ Theory. 2020 Dec;26:431-464. doi: 10.1007/s10588-019-09297-2. Epub 2019 Aug 12.
We propose, and test, a model of online media platform users' decisions to act on, and share, received information. Specifically, we focus on how of message content drive its spread. Our model is based on Fuzzy-Trace Theory (FTT), a leading theory of decision under risk. Per FTT, online content is mentally represented in two ways: verbatim (objective, but decontextualized, facts), and gist (subjective, but meaningful, interpretation). Although encoded in parallel, gist tends to drive behaviors more strongly than verbatim representations for most individuals. Our model uses factors derived from FTT to make predictions regarding which content is more likely to be shared, namely: a) different levels of mental representation, b) the motivational content of a message, c) difficulty of information processing (e.g., the ease with which a given message may be comprehended and, therefore, its gist extracted), and d) social values.
我们提出并测试了一个关于在线媒体平台用户对所接收信息采取行动并分享的决策模型。具体而言，我们关注消息内容的哪些方面推动其传播。我们的模型基于模糊痕迹理论（FTT），这是一种风险决策的主导理论。根据FTT，在线内容在心理上以两种方式呈现：逐字记录（客观但脱离上下文的事实）和要点（主观但有意义的解释）。虽然两者是并行编码的，但对于大多数人来说，要点往往比逐字记录更能强烈地驱动行为。我们的模型使用从FTT得出的因素来预测哪些内容更有可能被分享，即：a）不同层次的心理表征，b）消息的动机内容，c）信息处理的难度（例如，给定消息被理解并因此提取其要点的难易程度），以及d）社会价值观。
