An Empirical Analysis of Rumor Detection on Microblogs with Recurrent Neural Networks

Simple item page
dc.contributor.authorBugueno, Margarita
dc.contributor.authorSepulveda Villalobos Gabriel Andres
dc.contributor.authorMendoza Rocha Marcelo Gabriel
dc.contributor.authorMeiselwitz, G
dc.date.accessioned2024-01-10T14:24:56Z
dc.date.available2024-01-10T14:24:56Z
dc.date.issued2019
dc.description.abstractThe popularity of microblogging websites makes them important for information dissemination. The diffusion of large volumes of fake or unverified information could emerge and spread producing damage. Due to the ever-increasing volume of data and the nature of complex diffusion, automatic rumor detection is a very challenging task. Supervised classification and other approaches have been widely used to identify rumors in social media posts. However, despite achieving competitive results, only a few studies have delved into the nature of the problem itself in order to identify key empirical factors that allow defining both the baseline models and their performance. In this work, we learn discriminative features from tweets content and propagation trees by following their sequential propagation structure. To do this we study the performance of a number of architectures based on recursive neural networks conditioning for rumor detection. In addition, to ingest tweets into each network, we study the effect of two different word embeddings schemes: Glove and Google news skip-grams. Results on the Twitter16 dataset show that model performance depends on many empirical factors and that some specific experimental configurations consistently drive to better results.
dc.description.funderMillennium Institute for Foundational Research on Data
dc.description.funderproject BASAL
dc.fechaingreso.objetodigital2024-04-18
dc.format.extent18 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1007/978-3-030-21902-4_21
dc.identifier.eisbn978-3-030-21902-4
dc.identifier.eissn1611-3349
dc.identifier.isbn978-3-030-21901-7
dc.identifier.issn0302-9743
dc.identifier.urihttps://doi.org/10.1007/978-3-030-21902-4_21
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/80285
dc.identifier.wosidWOS:000656421300021
dc.information.autorucEscuela de Ingeniería; Mendoza Rocha Marcelo Gabriel; S/I; 1237020
dc.information.autorucEscuela de Ingeniería; Sepulveda Villalobos Gabriel Andres; S/I; 1030561
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final310
dc.pagina.inicio293
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartof11th International Conference on Social Computing and Social Media (SCSM) Held as Part of the 21st International Conference on Human-Computer Interaction (HCI International), JUL 26-31, 2019, Orlando, FL
dc.rightsacceso restringido
dc.subjectRumor detection
dc.subjectPropagation trees
dc.subjectEmpirical factors
dc.titleAn Empirical Analysis of Rumor Detection on Microblogs with Recurrent Neural Networks
dc.typecomunicación de congreso
dc.volumen11578
sipa.codpersvinculados1237020
sipa.codpersvinculados1030561
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
An empirical analysis of rumor detection on microblogs with recurrent neural networks.pdf
Size:
3.08 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de conferencia