基于深度迁移网络的Twitter谣言检测研究 *

[Objective] This paper proposes a new model to address the issue of insufficient data facing network rumors detection. [Methods] We proposed a deep transfer network based on the Multi-BiLSTM network as well as domain distributions of MMD statistics calculation. Then, we trained the model to learn the data loss of source domain and the distribution difference among domains. Finally, we realized the effective migration of label information across domains. [Results] Compared with two traditional rumor detection methods, the proposed model’s F1 index was increased by 10.3% and 8.5% respectively. [Limitations] The effect of transfer was not obvious in skewed data distribution and multiple domains. Conclusions] The proposed method could improve the rumor detection results. The deep transfer network could achieve positive outcomes among domains, and provide new directions for Internet rumor recognition.

Keywords： Rumor Detection ; Deep Transfer Network ; Multi-BiLSTM ; Domain Adaption ; Twitter