L-measure evaluation metric for fake information detection models with binary class imbalance

Abstract

Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle.