Identifying Influential Brokers on Social Media from Social Network Structure

TL;DR

This paper investigates the identification of influential brokers in social networks, revealing that they are largely distinct from source spreaders and central nodes, and demonstrates the effectiveness of node embeddings in predicting influential brokers.

Contribution

It introduces methods to identify influential brokers using centrality measures and node embeddings, highlighting their differences from source spreaders and central nodes.

Findings

Most influential source spreaders are not influential brokers.

Overlap between central nodes and influential brokers is less than 15%.

Node embedding features improve broker prediction accuracy.

Abstract

Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F$_1$ scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.