Not only precision, recall and F1 scores, we evaluate the performance using a ranking criterion, Recall@K. In this work, since we assume that we send articles to human fact-checking experts in order of the suspiciousness scores, Recall@K is suitable for evaluating the ability of models to properly rank the suspicious articles.
recall@K evaluates the propotion of the correct suspicious articles in the top-K ranked ones, where T is the number of the total articles in the test set, and b i is a binary value, i.e., 1 if the i-th ranked article is suspicious and 0 otherwise.