\mathsf{SAFE}: Similarity-Aware Multi-Modal Fake News Detection
Table 2: Performance of Methods in Detecting Fake News
['[EMPTY]', '[BOLD] Acc.', '[BOLD] LIWC† 0.822', '[BOLD] VGG-19≀ 0.649', '[BOLD] att-RNN‡ 0.769', 'SAFE∖ [BOLD] T≀ 0.674', 'SAFE∖ [BOLD] V† 0.721', 'SAFE∖ [BOLD] S‡ 0.796', 'SAFE∖ [BOLD] W‡ 0.738', 'SAFE‡  [BOLD] 0.874']
5.2.1 General Performance Analysis The general performance of SAFE and baselines are provided in Tab. Results indicate when predicting fake news, SAFE can outperform all baselines based on the accuracy values and F1 scores for both datasets. Based on PolitiFact data, the general performance of methods is SAFE>att-RNN≈LIWC>VGG-19; while for GossipCop data, such performance is SAFE>VGG-19 > att-RNN>LIWC. Note that multiple supervised learners (such as SVM, decision tree, logistic regression, and k-NN) have been used with LIWC in our experiments, where we present the best performance (obtained from random forest) in Tab.