1. School of Information, Central University of Finance and Economics, Beijing 100081, China 2. School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China
A new inductive microblog rumor detection method based on graph convolutional networks (GCN) was proposed to solve the problems faced by traditional GCN in rumor detection, such as the insufficient consideration of word semantic information and the difficulty of selecting pooling methods. Firstly, the semantic relationship between words was considered. A microblog event graph construction method based on word semantic correlation was proposed by combining the traditional word co-occurrence based graph construction method, and the node information aggregation was realized by combining GCN and gate recurrent unit (GRU). Then, in order to effectively fuse the feature information of different nodes, a multiple pooling methods fusion strategy based on attention mechanism, which fused max-pooling, average-pooling and global-pooling, was proposed to obtain the final graph level vector. Finally, in order to improve the efficiency of microblog rumor detection, the influence of microblog comment time on detection results was explored, and the best comment utilization time threshold for model training was obtained. Experimental results show that the performance of the proposed method is generally better than that of Text-CNN, Bi-GCN, TextING and other typical methods on the given datasets, verifying its effectiveness in the field of microblog rumor detection.
Tab.4Parameter settings of different methods for comparisons
方法
Ma_Dataset
Song_Dataset
Acc
Pre
Rec
F1
Acc
Pre
Rec
F1
DT-Rank
0.727
0.736
0.731
0.733
0.653
0.637
0.665
0.651
SVM-TS
0.829
0.814
0.823
0.818
0.746
0.751
0.761
0.756
Text-CNN
0.848
0.839
0.854
0.846
0.801
0.807
0.812
0.809
GRU-2
0.902
0.895
0.891
0.893
0.842
0.837
0.846
0.841
dEFEND
0.917
0.912
0.929
0.920
0.881
0.873
0.898
0.885
Text-GCN
0.924
0.915
0.919
0.917
0.889
0.892
0.885
0.888
Bi-GCN
0.929
0.931
0.924
0.927
0.901
0.897
0.906
0.901
GLAN
0.930
0.935
0.932
0.933
0.903
0.908
0.912
0.910
TextING
0.938
0.937
0.943
0.940
0.912
0.906
0.915
0.910
本研究方法
0.946
0.939
0.943
0.941
0.923
0.925
0.922
0.923
Tab.5Comparison of microblog rumor detection results of proposed method and existing typical methods
方法
Ma_Dataset
Song_Dataset
Acc
Pre
Rec
F1
Acc
Pre
Rec
F1
DT-Rank
0.723
0.728
0.725
0.726
0.647
0.635
0.669
0.652
SVM-TS
0.824
0.810
0.817
0.813
0.743
0.753
0.764
0.758
Text-CNN
0.839
0.833
0.849
0.841
0.800
0.813
0.809
0.811
GRU-2
0.899
0.896
0.885
0.890
0.839
0.835
0.847
0.841
dEFEND
0.915
0.913
0.931
0.922
0.877
0.869
0.899
0.883
Text-GCN
0.925
0.916
0.913
0.914
0.892
0.887
0.880
0.883
Bi-GCN
0.928
0.933
0.921
0.927
0.902
0.895
0.911
0.903
GLAN
0.929
0.936
0.930
0.933
0.902
0.907
0.916
0.911
TextING
0.937
0.936
0.939
0.937
0.909
0.908
0.911
0.909
本研究方法
0.945
0.938
0.941
0.940
0.921
0.920
0.925
0.923
Tab.6Validation of effectiveness of optimal comment utilization time threshold
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