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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 47-55    DOI: 10.11925/infotech.2096-3467.2018.1250
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Detecting Twitter Rumors with Deep Transfer Network
Kan Liu(),Haochen Du
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073,China
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[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.

Key wordsRumor Detection      Deep Transfer Network      Multi-BiLSTM      Domain Adaption      Twitter     
Received: 09 November 2018      Published: 25 November 2019
ZTFLH:  TP393  
Corresponding Authors: Kan Liu     E-mail:

Cite this article:

Kan Liu,Haochen Du. Detecting Twitter Rumors with Deep Transfer Network. Data Analysis and Knowledge Discovery, 2019, 3(10): 47-55.

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特征名 特征解释
reg_period 注册时间, 从注册日到数据获取日, 以月计
followers_count 作者粉丝数
friends_count 作者朋友数
listed_count 作者关注数
statuses_count 作者发布信息的数量
Description 作者个人描述的长度
Sex 作者性别
Location 用户位置
特征名 特征解释
retweet_count 转发数
retweet favorite_count 转发点赞数
retweet_comment_count 转发评论数
retweet_ followers_count 转发用户平均粉丝数
retweet_reg_period 转发用户平均注册时间
文本领域 谣言文本数(条) 非谣言文本数(条)
Politics 1 780 1 934
News 1 744 1 659
Food 562 676
History 488 440
Business 576 455
目标领域 建模方式 Precision Recall F1
Food 深度迁移学习(P>>F) 0.814 0.822 0.818
深度迁移学习(N>>F) 0.869 0.872 0.870
SVM(有监督学习) 0.881 0.875 0.878
LR(有监督学习) 0.873 0.869 0.871
Multi-AN(无监督学习) 0.802 0.801 0.801
RNN+AN(无监督学习) 0.823 0.813 0.818
History 深度迁移学习(P>>H) 0.874 0.869 0.871
深度迁移学习(N>>H) 0.865 0.872 0.868
SVM(有监督学习) 0.903 0.890 0.896
LR(有监督学习) 0.873 0.865 0.869
Multi-AN(无监督学习) 0.869 0.871 0.870
RNN+AN(无监督学习) 0.882 0.862 0.872
Business 深度迁移网络(P>>B) 0.901 0.895 0.898
深度迁移网络(N>>B) 0.904 0.884 0.894
SVM(有监督学习) 0.913 0.906 0.909
LR(有监督学习) 0.907 0.901 0.904
Multi-AN(无监督学习) 0.824 0.819 0.821
RNN+AN(无监督学习) 0.831 0.826 0.828
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