@inproceedings{f6d858382aea44fe9dfc51c610dde00c,
title = "Maximum Entropy Networks Applied on Twitter Disinformation Datasets",
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.",
keywords = "Disinformation, Maximum entropy networks, Twitter",
author = "\{De Clerck\}, Bart and \{Van Utterbeeck\}, Filip and Julien Petit and Ben Lauwens and Wim Mees and Rocha, \{Luis E.C.\}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; Conference date: 30-11-2021 Through 02-12-2021",
year = "2022",
doi = "10.1007/978-3-030-93413-2\_12",
language = "English",
isbn = "9783030934125",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "132--143",
editor = "Benito, \{Rosa Maria\} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, \{Luis M.\} and Marta Sales-Pardo",
booktitle = "Complex Networks and Their Applications X - Volume 2, Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021",
}