Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality

TL;DR

This paper presents a new method using Granger Causality within an Ego-Alter framework to analyze individual influence on Twitter, revealing how different users impact topics and misinformation spread.

Contribution

It introduces a novel person-to-person influence analysis method focusing on content influence, with applications in misinformation detection and targeted marketing.

Findings

Different Alters have varying influence scopes across topics.

The influence magnitude of Alters varies by topic.

Identifies key users who can signal misinformation early.

Abstract

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.