Home // International Journal On Advances in Internet Technology, volume 15, numbers 3 and 4, 2022 // View article

Truth or Fake? Developing a Taxonomical Framework for the Textual Detection of Online Disinformation

Authors:
Isabel Bezzaoui
Jonas Fegert
Christof Weinhardt

Keywords: Fake News; Disinformation Detection; Machine Learning-Based Systems; Taxonomy.

Abstract:
Disinformation campaigns have become a major threat to democracy and social cohesion. Phenomena like conspiracy theories promote political polarization; they can influence elections and lead people to (self-)damaging or even terrorist behavior. Since social media users and even larger platform operators are currently unready to precisely detect disinformation, new techniques for identifying online disinformation are urgently needed. In this paper, we present the first research insights of DeFaktS, an Information Systems research project, which takes a comprehensive approach to both researching and combating online disinformation with a special focus on enhancing media literacy and trust in explainable AI. Specifically, we demonstrate the first methodological steps towards the training of a machine learning-based system. This will be obtained by introducing the development and preliminary results of a taxonomy to support the labeling of a ‘Fake News’ dataset.

Pages: 53 to 63

Copyright: Copyright (c) to authors, 2022. Used with permission.

Publication date: December 31, 2022

Published in: journal

ISSN: 1942-2652