Machine Learning and Deep Learning Applications in Disinformation Detection: A Bibliometric Assessment

Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purpose of which is to manipulate and generate confusion among people in order to influence their opinion and obtain certain advantages in multiple domains (politics, economics, etc.). Propaganda, rumors, and conspiracy theories are just a few examples of common disinformation. Therefore, there is an urgent need to understand this phenomenon and offer the scientific community a paper that provides a comprehensive examination of the existing literature, lay the foundation for future research areas, and contribute to the fight against disinformation. The present manuscript provides a detailed bibliometric analysis of the articles oriented towards disinformation detection, involving high-performance machine learning and deep learning algorithms. The dataset has been collected from the popular Web of Science database, through the use of specific keywords such as “disinformation”, “machine learning”, or “deep learning”, followed by a manual check of the papers included in the dataset. The documents were examined using the popular R tool, Biblioshiny 4.2.0; the bibliometric analysis included multiple perspectives and various facets: dataset overview, sources, authors, papers, n-gram analysis, and mixed analysis. The results highlight an increased interest from the scientific community on disinformation topics in the context of machine learning and deep learning, supported by an annual growth rate of 96.1%. The insights gained from the research bring to light surprising details, while the study provides a solid basis for both future research in this area, as well for the development of new strategies addressing this complex issue of disinformation and ensuring a trustworthy and safe online environment.