Automated Fake News Detection in the Age of Digital Libraries

Creative Commons License

Mertoglu U., GENÇ B.

INFORMATION TECHNOLOGY AND LIBRARIES, vol.39, no.4, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.6017/ital.v39i4.12483
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CINAHL, Computer & Applied Sciences, EBSCO Education Source, Education Abstracts, Information Science and Technology Abstracts, Library and Information Science Abstracts, Library Literature and Information Science, MLA - Modern Language Association Database, Public Affairs Index, Directory of Open Access Journals, DIALNET, Library, Information Science & Technology Abstracts (LISTA)
  • Hacettepe University Affiliated: Yes


The transformation of printed media into the digital environment and the extensive use of social media have changed the concept of media literacy and people's habits of news consumption. While online news is faster, easier, comparatively cheaper, and offers convenience in terms of people's access to information, it speeds up the dissemination of fake news. Due to the free production and consumption of large amounts of data, fact-checking systems powered by human efforts are not enough to question the credibility of the information provided, or to prevent its rapid dissemination like a virus. Libraries, long known as sources of trusted information, are facing challenges caused by misinformation as mentioned in studies about fake news and libraries.(1) Considering that libraries are undergoing digitization processes all over the world and are providing digital media to their users, it is very likely that unverified digital content will be served by world's libraries. The solution is to develop automated mechanisms that can check the credibility of digital content served in libraries without manual validation. For this purpose, we developed an automated fake news detection system based on Turkish digital news content. Our approach can be modified for any other language if there is labelled training material. This model can be integrated into libraries' digital systems to label served news content as potentially fake whenever necessary, preventing uncontrolled falsehood dissemination via libraries.