Selective Intervention Strategy Based on Content Perception Model Against Fake News Sharing
Selective Intervention Strategy Based on Content Perception Model Against Fake News Sharing
复制标题
基于内容感知模型的针对假新闻分享的选择性干预策略
DOI：
10.1109/scisisis55246.2022.10002015
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
M.
中科院分区：
文献类型：
--
作者：
Fujimoto;K.;Tanaka;Y.;& Inuzuka;M.
本文提出了一种称为选择性干预的干预策略，该策略使用内容感知模型来分析针对虚假新闻共享的认知层面的干预效果。内容感知模型推导了内容和用户的每种组合的抑制效果的预期大小，因此它允许选择性干预，即从最大化预期抑制效果的角度选择性地使用干预类型。该模型被开发为贝叶斯网络，它将随机变量分配给五个层之一：干预、内容特征、感知特征、主动状态和被动状态。这里介绍两种干预类型：基于准确性的干预和基于校正的干预。基于计算机模拟技术，在先前研究的政治假新闻背景下，将选择性干预的有效性与其他简单干预策略的有效性进行了比较。
This paper presents an intervention strategy, called a selective intervention, designed using a content perception model to analyze intervention effects at the cognitive level against fake news sharing. The content perception model derives the expected size of the suppression effect for each combination of content and user, so it allows selective intervention, that is, the selective use of the type of intervention from the perspective of maximizing the expected suppression effect. The model is developed as a Bayesian network, which assigns random variables to one of five layers: intervention, content feature, perceived feature, active state, and passive state. Here, two intervention types are introduced: accuracy-nudge based and correction based interventions. Based on a computer simulation technique, the effectiveness of selective intervention is compared with those of other simple intervention strategies in the context of political fake news studied in a previous work.