We introduce a novel method for analyzing person-to-person content influence on Twitter. Using an Ego-Alter framework and Granger Causality, we examine President Donald Trump (the Ego) and the people he retweets (Alters) as a case study. We find that each Alter has a different scope of influence across multiple topics, different magnitude of influence on a given topic, and the magnitude of a single Alter's influence can vary across topics. This work is novel in its focus on person-to-person influence and content-based influence. Its impact is two-fold: (1) identifying "canaries in the coal mine" who could be observed by misinformation researchers or platforms to identify misinformation narratives before super-influencers spread them to large audiences, and (2) enabling digital marketing targeted toward upstream Alters of super-influencers.
翻译:我们引入了一种分析Twitter上人与人之间内容影响的新方法。我们使用Ego-Alter框架和Granger Causality,将Donald Trump总统(Ego)和他转接的人(Alters)作为案例研究进行审查。我们发现,每个Alter在多个专题上的影响范围不同,对特定专题的影响程度不同,单一Alter的影响程度各有不同。这项工作在侧重于人与人的影响和内容影响方面是新颖的。其影响有两重:(1) 确定“煤矿中的征兆”,错误的研究人员或平台可以在超级影响者向广大受众传播前观察这些征兆的错误叙事,(2) 使针对超级影响者上游流传的数字营销成为目标。