Maximum Entropy Networks Applied on Twitter Disinformation Datasets

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Identifying and detecting disinformation is a major challenge. Twitter provides datasets of disinformation campaigns through their information operations report. We compare the results of community detection using a classical network representation with a maximum entropy network model. We conclude that the latter method is useful to identify the most significant interactions in the disinformation network over multiple datasets. We also apply the method to a disinformation dataset related to COVID-19, which allows us to assess the repeatability of studies on disinformation datasets.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications X - Volume 2, Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
EditorsRosa Maria Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis M. Rocha, Marta Sales-Pardo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages132-143
Number of pages12
ISBN (Print)9783030934125
DOIs
Publication statusPublished - 2022
Event10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 - Madrid, Spain
Duration: 30 Nov 20212 Dec 2021

Publication series

NameStudies in Computational Intelligence
Volume1016
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021
Country/TerritorySpain
CityMadrid
Period30/11/212/12/21

Keywords

  • Disinformation
  • Maximum entropy networks
  • Twitter

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