Identifying Communication Topologies on Twitter
| Autor: | Huifen Zhong, Bowen Xue, Ben Niu, Mijat Kustudic, Lijing Tan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
information flow TK7800-8360 Computer Networks and Communications Computer science media_common.quotation_subject Node (networking) social media Network topology Variable (computer science) Betweenness centrality network structure Hardware and Architecture Control and Systems Engineering Signal Processing Cluster (physics) social network analytics Conversation Social media Electrical and Electronic Engineering Electronics Centrality media_common |
| Zdroj: | Electronics, Vol 10, Iss 2151, p 2151 (2021) Electronics Volume 10 Issue 17 |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|