Identifying Communication Topologies on Twitter

Mijat Kustudic,Huifen Zhong,Bowen Xue,Ben Niu,Lijing Tan

doi:10.3390/electronics10172151

Mijat Kustudic, Huifen Zhong + Show 3 more

Open Access

https://doi.org/10.3390/electronics10172151

Copy DOI

Abstract

Social networks are known for their decentralization and democracy. Each individual has a chance to participate and influence any discussion. Even with all the freedom, people’s behavior falls under patterns that are observed in numerous situations. In this paper, we propose a methodology that defines and searches for common communication patterns in topical networks on Twitter. We analyze clusters according to four traits: number of nodes the cluster has, their degree and betweenness centrality values, number of node types, and whether the cluster is open or closed. We find that cluster structures can be defined as (a) fixed, meaning that they are repeated across datasets/topics following uniform rules, or (b) variable if they follow an underlying rule regardless of their size. This approach allows us to classify 90% of all conversation clusters, with the number varying by topic. An increase in cluster size often results in difficulties finding topological shape rules; however, these types of clusters tend to exhibit rules regarding their node relationships in the form of centralization. Most individuals do not enter large-scale discussions on Twitter, meaning that the simplicity of communication clusters implies repetition. In general, power laws apply for the influencer connection distribution (degree centrality) even in topical networks.

Highlights

With the advent of the internet, information can be generated with or without a monetary cost [1]
We analyzed 162 twitter datasets obtained from the NodeXL database
When more nodes are added, which are not connected to the main cluster, the topologies evolve into the brand or community type that had a high number of clusters and nodes

Summary

Introduction

With the advent of the internet, information can be generated with or without a monetary cost [1]. The majority of content is created and distributed by participants and peers Due to this fact, early researchers have speculated that an online democracy will be created where “citizens and political leaders interact in new and exciting ways” [2]. Online everyone starts the same, and no central authority governs the whole internet; overseeing is done on platforms This means that some of these egalitarian predictions of early researchers came true: prominent figures, such as state-affiliated accounts [5] or the account of the U.S president [6], are treated to a regular person on social networks such as Twitter, regardless of their real-world power. Their existence and properties in online communities can have far-reaching consequences for many processes that unfold on networks [9], influencing individuals’ underlying activity and overall evolution [10]

Methods

Results

Discussion

Conclusion