Measuring user influence on Twitter: A survey

TL;DR

This survey reviews various measures of user influence on Twitter, analyzing their computational efficiency, relevance classification, and diversity, including simple metrics, PageRank-based, content-focused, and predictive models.

Contribution

It provides a comprehensive classification and comparison of Twitter influence measures, highlighting their diversity and computational aspects.

Findings

Many influence measures are based on PageRank and activity metrics.

Measures vary in complexity from simple API metrics to complex models.

The survey identifies key aspects affecting influence measurement accuracy.

Abstract

Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.