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First public dataset to study 2023 Turkish general election.
Najafi, Ali; Mugurtay, Nihat; Zouzou, Yasser; Demirci, Ege; Demirkiran, Serhat; Karadeniz, Huseyin Alper; Varol, Onur.
Afiliação
  • Najafi A; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Mugurtay N; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Zouzou Y; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Demirci E; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Demirkiran S; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Karadeniz HA; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey.
  • Varol O; Faculty of Engineering and Natural Sciences, Sabanci University, 34456, Istanbul, Turkey. onur.varol@sabanciuniv.edu.
Sci Rep ; 14(1): 8794, 2024 Apr 16.
Article em En | MEDLINE | ID: mdl-38627434
ABSTRACT
In the context of Turkiye's most recent parliamentary and presidential elections ("seçim" in Turkish), social media has played an important role in shaping public debate. It is of utmost importance to capture social media trends during the 2023 Turkish elections, since it uncovers a great deal of information of election propaganda, political debates, smear campaigns, and election manipulation by domestic and international actors. We provide a comprehensive dataset for social media researchers to study Turkish elections, develop tools to prevent online manipulation, and gather novel information to inform the public. We are committed to continually improving the data collection and updating it regularly leading up to the election. Using the #Secim2023 dataset, researchers can examine the social and communication networks between political actors, track current trends, and investigate emerging threats to election integrity. Our dataset and analysis code available through Harvard Dataverse and Github, respectively.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Turquia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Turquia