Towards understanding the role of content-based and contextualized features in detecting abuse on Twitter.

Hussain, Kamal; Saeed, Zafar; Abbasi, Rabeeh; Sindhu, Muddassar; Khattak, Akmal; Arafat, Sachi; Daud, Ali; Mushtaq, Mubashar

Hussain, Kamal; Saeed, Zafar; Abbasi, Rabeeh; Sindhu, Muddassar; Khattak, Akmal; Arafat, Sachi; Daud, Ali; Mushtaq, Mubashar.

Affiliation

Hussain K; Instituto Superior Técnico, Universidade de Lisboa, Portugal.
Saeed Z; Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy.
Abbasi R; Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
Sindhu M; Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
Khattak A; Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
Arafat S; Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Daud A; Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates.
Mushtaq M; Department of Computer Science, Forman Christian College, Lahore, Pakistan.

Heliyon ; 10(8): e29593, 2024 Apr 30.

Article de En | MEDLINE | ID: mdl-38665572

Mots clés

Abuse; Context; Machine learning; Social media; Twitter

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Heliyon Année: 2024 Type de document: Article Pays d'affiliation: Portugal Pays de publication: Royaume-Uni

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