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Table 3 Results of comparison with different models on twitter15 datasets

From: KAGN:knowledge-powered attention and graph convolutional networks for social media rumor detection

Method

Acc

NR

FR

TR

UR

F_1

F_1

F_1

F_1

DTC

0.454

0.733

0.355

0.317

0.415

RFC

0.565

0.81

0.422

0.401

0.543

SVM-TS

0.544

0.796

0.472

0.404

0.483

PTK

0.75

0.804

0.698

0.765

0.733

GRU

0.646

0.792

0.574

0.608

0.592

BU-RvNN

0.708

0.695

0.728

0.759

0.653

TD-RvNN

0.723

0.682

0.758

0.821

0.654

PPC

0.842

0.811

0.875

0.818

0.79

Bi-GCN

0.886

0.891

0.86

0.93

0.864

KAGN

0.892

0.868

0.883

0.894

0.927

  1. NR non-rumor, FR false rumor, TR true rumor, UR unverified rumor