Patterns, Volume 2, 12 November 2021,
In this article, we pursue the automatic detection of fake news reporting on the Syrian war using machine learning and meta-learning. The proposed approach is based on a suite of features that include a given article's linguistic style; its level of subjectivity, sensationalism, and sectarianism; the strength of its attribution; and its consistency with other news articles from the same “media camp”. To train our models, we use FA-KES, a fake news dataset about the Syrian war. A suite of basic machine learning models is explored, as well as the model-agnostic meta-learning algorithm (MAML) suitable for few-shot learning, using datasets of a modest size. Feature-importance analysis confirms that the collected features specific to the Syrian war are indeed very important predictors for the output label. The meta-learning model achieves the best performance, improving upon the baseline approaches that are trained exclusively on text features in FA-KES.
DSML 3: Development/pre-production: Data Science Output Has Been Rolled Out/validated Across Multiple Domains/problems; DSML 3: Development/pre-production: Data Science Output Have Been Rolled Out/validated Across Multiple Domain/problem; Domain Problems; Fake News Detection; Feature Importance; Feature Selection; Features Selection; Learning Algorithms; Machine Learning; Meta-learning; Metalearning; Multiple Domains; Pre-production; Production Data; Syrian War; Global