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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (7): 101-110    DOI: 10.11925/infotech.2096-3467.2020.1216
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Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism
Yang Hanxun,Zhou Dequn,Ma Jing(),Luo Yongcong
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
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Abstract  

[Objective] This paper proposes a new model with the help of uncertainty loss function and task-level attention mechanism, aiming to address the issue of setting main and auxiliary tasks in rumor detection. [Methods] First, we integrated the domain knowledge of rumor exploration, stance classification, and rumor detectioin. Then, we constructed a modified model with task-level attention mechanism. Third, we used uncertainty loss function to explore the weight relationshaip of each task and obtain better detection results. Finally, we examined our model’s performance with the Pheme4 and Pheme5 datasets. [Results] Compared to the exisiting models, the Macro-F of our model increased by 4.2 and 7.6 percentage points with Pheme4 and Pheme5. [Limitations] We only examined our model with the Pheme dataset. [Conclusions] The proposed method could effective detect rumors without dividing the main and auxiliary tasks.

Key wordsUncertain Loss      Multi-task Learning      Rumor Detection      Attention Mechanism     
Received: 06 December 2020      Published: 09 April 2021
ZTFLH:  TP393  
Fund:Forward Development Strategy Research Fund Project(NW2020001);National Social Science Fund of China(20ZDA092);Open Fund Project of Nanjing University of Aeronautics and Astronautics Graduate Innovation Base (Laboratory)(kfjj20200901)
Corresponding Authors: Ma Jing,ORCID: 0000-0001-8472-2518     E-mail: majing5525@126.com

Cite this article:

Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism. Data Analysis and Knowledge Discovery, 2021, 5(7): 101-110.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1216     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I7/101

Rumor Identification Process
Multitask Model
Branch_LSTM Model
Attention Mechanisms Based on Task Hierarchy
事件名称 事件文本量 回复文本量 疑似谣言 非疑似谣言 谣言 非谣言 无法判定
Charlie Hebdo 2 079 38 268 458 1621 193 116 149
Sydney Siege 1 221 23 996 522 699 382 86 54
Ferguson 1 143 24 175 284 859 10 8 266
Ottawa Shooting 890 12 284 470 420 329 72 69
Germanwings-crash 469 4 489 238 231 94 111 33
Putin Missing 238 835 126 112 0 9 117
Prince Toronto 233 902 229 4 0 222 7
Gurlitt 138 179 61 77 59 0 2
Elbola Essien 14 226 14 0 0 14 0
合计 6 425 105 354 2 402 4 023 1 067 638 697
PHEME Dataset
A Tree Structure with Three Branches of Rumor Text and Comments
操作系统配置 参数或版本
CPU Xeon(R) Gold 5218 CPU
GPU NVIDIA T4(16GB)
Python 3.6
TensorFlow 1.1.31
Keras 2.3.1
内存 1TB
Experimental Environment
实验 算法 acc Macro-F
实验一 Majority(True) 0.591 0.247
NileTMRG* 0.444 0.205
Branch-LSTM 0.466 0.362
MTL3 0.462 0.322
ES-ATT-MTL3 0.395 0.263
Task-ATT-MTL3 0.494 0.333
Un-Task-ATT-MTL3 0.425 0.364
实验二 Majority(True) 0.511 0.226
NileTMRG* 0.438 0.339
Branch-LSTM 0.454 0.336
MTL3 0.492 0.396
ES-ATT-MTL3 0.459 0.280
Task-ATT-MTL3 0.505 0.372
Un-Task-ATT-MTL3 0.467 0.472
实验三 Majority(True) 0.444 0.205
NileTMRG* 0.360 0.297
Branch-LSTM 0.314 0.259
MTL3 0.405 0.405
ES-ATT-MTL3 0.356 0.240
Task-ATT-MTL3 0.418 0.347
Un-Task-ATT-MTL3 0.385 0.393
Results of Rumor Detection Task
事件 acc Macro-F 谣言类
平均
F1
非谣言类平均F1 无法确定类平均F1
Charlie Hebdo 0.292 0.213 0.147 0.131 0.362
Sydney Siege 0.339 0.204 0.400 0.162 0.501
Ferguson 0.697 0.268 0 0.004 0.801
Ottawa Shooting 0.675 0.306 0.806 0.107 0.010
Germanwings-crash 0.356 0.245 0.310 0.355 0.091
Results of Single Event
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