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dc.contributor.authorMouronte López, Mary Luz 
dc.date.accessioned2022-08-03T11:52:33Z
dc.date.available2022-08-03T11:52:33Z
dc.date.issued2022
dc.identifier.issn1076-2787spa
dc.identifier.urihttp://hdl.handle.net/10641/3081
dc.description.abstract)is work studies the interaction networks (replying, retweeting, and quoting) that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. )e largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. )e quoting and retweeting networks have a more hierarchical structure than the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled (with high accuracy) through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. )ecumulative probability distributions of hashtags per community are also examined.spa
dc.language.isoengspa
dc.publisherComplexityspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleModeling the Interaction Networks about the Climate Change on Twitter: A Characterization of its Network Structure.spa
dc.typearticlespa
dc.description.versionpost-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent3710 KBspa
dc.identifier.doi10.1155/2022/8924468spa
dc.relation.publisherversionhttps://www.hindawi.com/journals/complexity/2022/8924468/spa


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España