2021/10/10 by Richard Kuzma, Kuzma, Richard, Iain J. Cruickshank +3
Computer Science · Physics and Astronomy · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Misinformation and Its Impacts #Opinion Dynamics and Social Influence #Social Media and Politics #Social and Information Networks (cs.SI) #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.2110.04899
arxiv created 2021/10/10 · openalex publication_date 2021/10/10 · arxiv updated 2021/10/12 · openalex created_date 2023/03/20 · openalex updated_date 2026/07/28
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.