Detection of hateful twitter users with graph convolutional network model

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dc.authoridUTKU, Anil/0000-0002-7240-8713|CAN, UMIT/0000-0002-8832-6317|ASLAN, SERPIL/0000-0001-8009-063X
dc.contributor.authorUtku, Anil
dc.contributor.authorCan, Umit
dc.contributor.authorAslan, Serpil
dc.date.accessioned2025-10-24T18:08:52Z
dc.date.available2025-10-24T18:08:52Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractToday, hate speech is widespread and persistent in various forms on social networking platforms, targeting different minority groups. These attacks can be carried out using various factors such as racial, religious, gender, and physical disability, etc. Considering the number of people and their interactions, social networks are the most important channels through which these discourses spread. The social network structure is considered a set of nodes and edges and is very suitable for the graph structure. The multidimensional structure of social networks carries social network data from Euclidean space to non-Euclidean space. In non-Euclidean space, the graph structure is used to represent data effectively. In this respect, solving the hate speech problem with graph-based methods in a complex dimensional space can produce more impressive results. In this study, a powerful method based on the Graph Convolutional Network (GCN) model, which is rarely used in this field, was proposed for the detection of hateful Twitter users in social networks. Well-known machine learning methods were used to measure the performance of this method. According to the results obtained, the proposed GCN model gave the most successful result.
dc.identifier.doi10.1007/s12145-023-00940-w
dc.identifier.endpage343
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue1
dc.identifier.startpage329
dc.identifier.urihttps://doi.org/10.1007/s12145-023-00940-w
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3350
dc.identifier.volume16
dc.identifier.wosWOS:000915867600002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20251023
dc.subjectHate speech detection; Graph convolutional network; Deep learning; Machine learning
dc.titleDetection of hateful twitter users with graph convolutional network model
dc.typeArticle

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