Analysing centralities for organisational role inference in online social networks

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dc.contributor.authorSánchez Corcuera, Rubén
dc.contributor.authorBilbao Jayo, Aritz
dc.contributor.authorZulaika Zurimendi, Unai
dc.contributor.authorAlmeida, Aitor
dc.date.accessioned2024-11-04T16:01:27Z
dc.date.available2024-11-04T16:01:27Z
dc.date.issued2021-03
dc.date.updated2024-11-04T16:01:27Z
dc.description.abstractThe intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.en
dc.description.sponsorshipWe gratefully acknowledge the support of the Basque Governments Department of Education, Spain for the predoctoral funding of some of the authors and for DEUSTEK4: Entornos inteligentes abiertos y tecnologías para el aprendizaje, recognised group of the Basque University System , IT1078-16 . We also gratefully acknowledge the support of NVIDIA Corporation, Spain for the donation of the hardware used in this research. Finally, we also wanted to acknowledge the Weight and Biases ( Biewald, 2020 ) team for their service and support.en
dc.identifier.citationSánchez-Corcuera, R., Bilbao-Jayo, A., Zulaika, U., & Almeida, A. (2021). Analysing centralities for organisational role inference in online social networks. Engineering Applications of Artificial Intelligence, 99. https://doi.org/10.1016/J.ENGAPPAI.2020.104129
dc.identifier.doi10.1016/J.ENGAPPAI.2020.104129
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1643
dc.language.isoeng
dc.publisherElsevier Ltd
dc.rights© 2020 The Author(s)
dc.subject.otherAdversarial information retrieval
dc.subject.otherGraph centralities
dc.subject.otherInformation inference
dc.subject.otherOnline social networks
dc.titleAnalysing centralities for organisational role inference in online social networksen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleEngineering Applications of Artificial Intelligence
oaire.citation.volume99
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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