Detection of hateful twitter users with graph convolutional network model

Basit Öğe Kaydı
dc.authoridASLAN, SERPIL/0000-0001-8009-063X
dc.authoridCAN, UMIT/0000-0002-8832-6317
dc.authorwosidUtku, Anıl/HCH-3214-2022
dc.contributor.authorUtku, Anil
dc.contributor.authorCan, Umit
dc.contributor.authorAslan, Serpil
dc.date.accessioned2024-08-04T20:57:36Z
dc.date.available2024-08-04T20:57:36Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.identifier.doi10.1007/s12145-023-00940-w
dc.identifier.endpage343en_US
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue1en_US
dc.identifier.startpage329en_US
dc.identifier.urihttps://doi.org/10.1007/s12145-023-00940-w
dc.identifier.urihttps://hdl.handle.net/11616/102772
dc.identifier.volume16en_US
dc.identifier.wosWOS:000915867600002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEarth Science Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHate speech detectionen_US
dc.subjectGraph convolutional networken_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.titleDetection of hateful twitter users with graph convolutional network modelen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu