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

Margarita Bugueño, Gabriel Sepulveda, Marcelo Mendoza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

© 2019, Springer Nature Switzerland AG. The 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.
Original languageEnglish
Title of host publicationAn Empirical Analysis of Rumor Detection on Microblogs with Recurrent Neural Networks
Pages293-310
Number of pages18
ISBN (Electronic)9783030219017
DOIs
Publication statusPublished - 1 Jan 2019
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11578 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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  • Cite this

    Bugueño, M., Sepulveda, G., & Mendoza, M. (2019). An Empirical Analysis of Rumor Detection on Microblogs with Recurrent Neural Networks. In An Empirical Analysis of Rumor Detection on Microblogs with Recurrent Neural Networks (pp. 293-310). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11578 LNCS). https://doi.org/10.1007/978-3-030-21902-4_21