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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.
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Received: 06 December 2020
Published: 09 April 2021
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| 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) |
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Corresponding Authors:
Ma Jing,ORCID： 0000-0001-8472-2518
E-mail: majing5525@126.com
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