Nowadays, big volumes of User-Generated Content (UGC) spread across various kinds of social media. In microblogging, UCG can be generated in the form of ‘newsworthy’ posts, i.e., related to information that has a public utility for the people. In this context, being the UGC diffused without almost any traditional form of trusted external control, the possibility of incurring in possible fake news is far from remote. For this reason, several approaches for fake news detection in microblogging have been proposed up to now, mostly based on machine learning techniques. In this paper, an ongoing work based on the use of the Multi-Criteria Decision Making (MCDM) paradigm to detect fake news is proposed. The aim is to reduce data dependency in building the model, and to have flexible control over the choices behind the fake news detection process.
CITATION STYLE
De Grandis, M., Pasi, G., & Viviani, M. (2019). Fake News Detection in Microblogging Through Quantifier-Guided Aggregation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11676 LNAI, pp. 64–76). Springer Verlag. https://doi.org/10.1007/978-3-030-26773-5_6
Mendeley helps you to discover research relevant for your work.