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Maximum Entropy Networks Applied on Twitter Disinformation Datasets
10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; 1016:132-143, 2022.
Article in English | Scopus | ID: covidwho-1627059
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 Year: 2022 Document Type: Article