Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2674-2682.DOI: 10.11772/j.issn.1001-9081.2023091359
• Artificial intelligence • Previous Articles Next Articles
Jinjin LI, Guoming SANG(), Yijia ZHANG
Received:
2023-10-09
Revised:
2023-12-08
Accepted:
2023-12-11
Online:
2024-03-21
Published:
2024-09-10
Contact:
Guoming SANG
About author:
LI Jinjin, born in 2000, M. S. candidate. Her research interests include natural language processing, rumor detection.Supported by:
通讯作者:
桑国明
作者简介:
李金金(2000—),女,河南漯河人,硕士研究生,CCF会员,主要研究方向:自然语言处理、谣言检测基金资助:
CLC Number:
Jinjin LI, Guoming SANG, Yijia ZHANG. Multi-domain fake news detection model enhanced by APK-CNN and Transformer[J]. Journal of Computer Applications, 2024, 44(9): 2674-2682.
李金金, 桑国明, 张益嘉. APK-CNN和Transformer增强的多域虚假新闻检测模型[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2674-2682.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091359
领域 | 样本数 | 领域 | 样本数 | ||
---|---|---|---|---|---|
真新闻 | 假新闻 | 真新闻 | 假新闻 | ||
科技 | 143 | 93 | 健康 | 485 | 515 |
军事 | 121 | 222 | 金融 | 959 | 362 |
教育 | 243 | 248 | 娱乐 | 1 000 | 440 |
灾难 | 185 | 591 | 社会 | 1 198 | 1 471 |
政治 | 306 | 546 | 合计 | 4 640 | 4 488 |
Tab. 1 Data distribution of Chinese dataset Ch-9
领域 | 样本数 | 领域 | 样本数 | ||
---|---|---|---|---|---|
真新闻 | 假新闻 | 真新闻 | 假新闻 | ||
科技 | 143 | 93 | 健康 | 485 | 515 |
军事 | 121 | 222 | 金融 | 959 | 362 |
教育 | 243 | 248 | 娱乐 | 1 000 | 440 |
灾难 | 185 | 591 | 社会 | 1 198 | 1 471 |
政治 | 306 | 546 | 合计 | 4 640 | 4 488 |
领域 | 样本数 | |
---|---|---|
真新闻 | 假新闻 | |
合计 | 22 001 | 6 763 |
Gossipcop | 16 804 | 5 067 |
Politifact | 447 | 379 |
COVID | 4 750 | 1 317 |
Tab. 2 Data distribution of English dataset En-3
领域 | 样本数 | |
---|---|---|
真新闻 | 假新闻 | |
合计 | 22 001 | 6 763 |
Gossipcop | 16 804 | 5 067 |
Politifact | 447 | 379 |
COVID | 4 750 | 1 317 |
模型 | 不同领域上的F1 | overall | |||||
---|---|---|---|---|---|---|---|
Gossipcop | Politifact | COVID | F1 | Acc | AUC | ||
单 域 | BIGRU | 76.66 | 77.22 | 88.85 | 79.58 | 86.68 | 88.40 |
TextCNN | 77.86 | 80.11 | 90.40 | 80.79 | 86.92 | 90.23 | |
RoBERTa | 78.10 | 85.83 | 92.88 | 81.84 | 88.02 | 91.08 | |
混 合 域 | BIGRU | 74.79 | 73.39 | 74.48 | 75.01 | 83.21 | 85.04 |
TextCNN | 75.19 | 70.40 | 83.22 | 76.79 | 83.62 | 86.74 | |
RoBERTa | 78.23 | 79.67 | 90.14 | 81.01 | 87.44 | 90.58 | |
StyleLSTM | 80.07 | 79.37 | 92.52 | 82.85 | 88.26 | 92.50 | |
DualEmo | 80.56 | 78.68 | 90.19 | 82.70 | 88.18 | 92.51 | |
多 域 | EANN | 79.37 | 75.58 | 88.36 | 81.23 | 87.43 | 90.53 |
MMoE | 80.22 | 84.77 | 93.79 | 83.61 | 89.20 | 92.65 | |
MoSE | 79.81 | 93.26 | 83.18 | 88.85 | 92.52 | ||
EDDFN | 80.67 | 85.05 | 93.06 | 83.78 | 89.12 | 92.63 | |
MDFEND | 80.80 | 84.73 | 93.31 | 83.90 | 89.36 | 92.37 | |
M3FEND | 84.78 | ||||||
Transm3 | 84.67 | 89.82 | 94.86 | 90.92 | 92.12 | 96.54 |
Tab. 3 Experimental results of different models on En-3 dataset
模型 | 不同领域上的F1 | overall | |||||
---|---|---|---|---|---|---|---|
Gossipcop | Politifact | COVID | F1 | Acc | AUC | ||
单 域 | BIGRU | 76.66 | 77.22 | 88.85 | 79.58 | 86.68 | 88.40 |
TextCNN | 77.86 | 80.11 | 90.40 | 80.79 | 86.92 | 90.23 | |
RoBERTa | 78.10 | 85.83 | 92.88 | 81.84 | 88.02 | 91.08 | |
混 合 域 | BIGRU | 74.79 | 73.39 | 74.48 | 75.01 | 83.21 | 85.04 |
TextCNN | 75.19 | 70.40 | 83.22 | 76.79 | 83.62 | 86.74 | |
RoBERTa | 78.23 | 79.67 | 90.14 | 81.01 | 87.44 | 90.58 | |
StyleLSTM | 80.07 | 79.37 | 92.52 | 82.85 | 88.26 | 92.50 | |
DualEmo | 80.56 | 78.68 | 90.19 | 82.70 | 88.18 | 92.51 | |
多 域 | EANN | 79.37 | 75.58 | 88.36 | 81.23 | 87.43 | 90.53 |
MMoE | 80.22 | 84.77 | 93.79 | 83.61 | 89.20 | 92.65 | |
MoSE | 79.81 | 93.26 | 83.18 | 88.85 | 92.52 | ||
EDDFN | 80.67 | 85.05 | 93.06 | 83.78 | 89.12 | 92.63 | |
MDFEND | 80.80 | 84.73 | 93.31 | 83.90 | 89.36 | 92.37 | |
M3FEND | 84.78 | ||||||
Transm3 | 84.67 | 89.82 | 94.86 | 90.92 | 92.12 | 96.54 |
模型 | 不同领域的F1 | overall | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
科技 | 军事 | 教育 | 灾难 | 政治 | 健康 | 金融 | 娱乐 | 社会 | F1 | Acc | AUC | ||
单 域 | BIGRU | 51.75 | 33.65 | 74.16 | 72.93 | 85.88 | 83.73 | 81.37 | 79.92 | 79.18 | 81.03 | 81.03 | 89.02 |
TextCNN | 40.74 | 33.65 | 80.59 | 43.88 | 84.82 | 88.19 | 82.15 | 79.73 | 86.15 | 83.69 | 83.70 | 90.94 | |
RoBERTa | 74.63 | 73.69 | 81.46 | 75.47 | 80.44 | 88.73 | 83.61 | 85.13 | 83.00 | 84.77 | 84.77 | 92.26 | |
混 合 域 | BIGRU | 72.69 | 87.24 | 81.38 | 79.35 | 83.56 | 88.68 | 82.91 | 86.29 | 84.85 | 85.95 | 85.98 | 93.09 |
TextCNN | 72.54 | 88.39 | 83.62 | 82.22 | 85.61 | 87.68 | 86.38 | 84.56 | 85.40 | 86.86 | 86.87 | 93.81 | |
RoBERTa | 77.77 | 90.72 | 83.31 | 85.12 | 83.66 | 90.90 | 87.35 | 87.69 | 85.77 | 87.95 | 87.97 | 94.51 | |
StyleLSTM | 77.29 | 91.87 | 83.41 | 85.32 | 84.87 | 90.84 | 88.02 | 88.46 | 85.52 | 88.20 | 88.21 | 94.71 | |
DualEmo | 83.23 | 90.26 | 83.62 | 83.96 | 84.55 | 89.05 | 89.44 | 85.69 | 88.46 | 88.46 | 95.41 | ||
多 域 | EANN | 82.25 | 92.74 | 86.24 | 86.66 | 87.05 | 91.05 | 87.10 | 89.57 | 88.77 | 89.75 | 89.77 | 96.10 |
MMoE | 91.12 | 87.06 | 87.70 | 86.20 | 93.64 | 85.67 | 88.86 | 87.50 | 89.47 | 89.48 | 95.47 | ||
MoSE | 85.02 | 88.58 | 88.15 | 86.72 | 88.08 | 91.79 | 86.72 | 89.13 | 87.29 | 89.39 | 89.40 | 95.43 | |
EDDFN | 81.86 | 91.37 | 86.76 | 87.86 | 84.78 | 93.79 | 86.36 | 88.32 | 86.89 | 89.19 | 89.19 | 95.28 | |
MDFEND | 83.01 | 93.89 | 89.17 | 94.00 | 89.51 | 90.66 | 89.80 | 91.37 | 91.38 | 97.08 | |||
M3FEND | 82.92 | 88.96 | 88.25 | 90.09 | |||||||||
Transm3 | 89.43 | 98.07 | 91.11 | 92.36 | 90.74 | 96.90 | 92.56 | 94.90 | 91.95 | 95.55 | 95.57 | 98.95 |
Tab. 4 Experimental results of different models on Ch-9 dataset
模型 | 不同领域的F1 | overall | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
科技 | 军事 | 教育 | 灾难 | 政治 | 健康 | 金融 | 娱乐 | 社会 | F1 | Acc | AUC | ||
单 域 | BIGRU | 51.75 | 33.65 | 74.16 | 72.93 | 85.88 | 83.73 | 81.37 | 79.92 | 79.18 | 81.03 | 81.03 | 89.02 |
TextCNN | 40.74 | 33.65 | 80.59 | 43.88 | 84.82 | 88.19 | 82.15 | 79.73 | 86.15 | 83.69 | 83.70 | 90.94 | |
RoBERTa | 74.63 | 73.69 | 81.46 | 75.47 | 80.44 | 88.73 | 83.61 | 85.13 | 83.00 | 84.77 | 84.77 | 92.26 | |
混 合 域 | BIGRU | 72.69 | 87.24 | 81.38 | 79.35 | 83.56 | 88.68 | 82.91 | 86.29 | 84.85 | 85.95 | 85.98 | 93.09 |
TextCNN | 72.54 | 88.39 | 83.62 | 82.22 | 85.61 | 87.68 | 86.38 | 84.56 | 85.40 | 86.86 | 86.87 | 93.81 | |
RoBERTa | 77.77 | 90.72 | 83.31 | 85.12 | 83.66 | 90.90 | 87.35 | 87.69 | 85.77 | 87.95 | 87.97 | 94.51 | |
StyleLSTM | 77.29 | 91.87 | 83.41 | 85.32 | 84.87 | 90.84 | 88.02 | 88.46 | 85.52 | 88.20 | 88.21 | 94.71 | |
DualEmo | 83.23 | 90.26 | 83.62 | 83.96 | 84.55 | 89.05 | 89.44 | 85.69 | 88.46 | 88.46 | 95.41 | ||
多 域 | EANN | 82.25 | 92.74 | 86.24 | 86.66 | 87.05 | 91.05 | 87.10 | 89.57 | 88.77 | 89.75 | 89.77 | 96.10 |
MMoE | 91.12 | 87.06 | 87.70 | 86.20 | 93.64 | 85.67 | 88.86 | 87.50 | 89.47 | 89.48 | 95.47 | ||
MoSE | 85.02 | 88.58 | 88.15 | 86.72 | 88.08 | 91.79 | 86.72 | 89.13 | 87.29 | 89.39 | 89.40 | 95.43 | |
EDDFN | 81.86 | 91.37 | 86.76 | 87.86 | 84.78 | 93.79 | 86.36 | 88.32 | 86.89 | 89.19 | 89.19 | 95.28 | |
MDFEND | 83.01 | 93.89 | 89.17 | 94.00 | 89.51 | 90.66 | 89.80 | 91.37 | 91.38 | 97.08 | |||
M3FEND | 82.92 | 88.96 | 88.25 | 90.09 | |||||||||
Transm3 | 89.43 | 98.07 | 91.11 | 92.36 | 90.74 | 96.90 | 92.56 | 94.90 | 91.95 | 95.55 | 95.57 | 98.95 |
数据集 | Head | F1/% | Acc/% | 数据集 | Head | F1/% | Acc/% |
---|---|---|---|---|---|---|---|
En-3 | 1 | 87.45 | 88.01 | Ch-9 | 1 | 91.14 | 91.15 |
2 | 89.66 | 88.97 | 2 | 93.20 | 93.19 | ||
4 | 90.92 | 92.12 | 4 | 95.55 | 95.57 | ||
8 | 88.12 | 89.63 | 8 | 92.35 | 92.35 |
Tab. 5 Impact of head number of attention mechanism on model performance
数据集 | Head | F1/% | Acc/% | 数据集 | Head | F1/% | Acc/% |
---|---|---|---|---|---|---|---|
En-3 | 1 | 87.45 | 88.01 | Ch-9 | 1 | 91.14 | 91.15 |
2 | 89.66 | 88.97 | 2 | 93.20 | 93.19 | ||
4 | 90.92 | 92.12 | 4 | 95.55 | 95.57 | ||
8 | 88.12 | 89.63 | 8 | 92.35 | 92.35 |
数据集 | layer | F1/% | Acc/% | 数据集 | layer | F1/% | Acc/% |
---|---|---|---|---|---|---|---|
En-3 | 1 | 90.92 | 92.12 | Ch-9 | 1 | 95.55 | 95.57 |
2 | 89.12 | 91.13 | 2 | 92.12 | 92.13 | ||
4 | 87.98 | 89.98 | 4 | 90.98 | 90.98 | ||
8 | 83.94 | 86.91 | 8 | 89.94 | 89.99 |
Tab. 6 Impact of number of Encoder layers on model performance
数据集 | layer | F1/% | Acc/% | 数据集 | layer | F1/% | Acc/% |
---|---|---|---|---|---|---|---|
En-3 | 1 | 90.92 | 92.12 | Ch-9 | 1 | 95.55 | 95.57 |
2 | 89.12 | 91.13 | 2 | 92.12 | 92.13 | ||
4 | 87.98 | 89.98 | 4 | 90.98 | 90.98 | ||
8 | 83.94 | 86.91 | 8 | 89.94 | 89.99 |
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