Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 47-55    DOI: 10.11925/infotech.2096-3467.2018.1250
Current Issue | Archive | Adv Search |
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
Download: PDF(705 KB)   HTML ( 25
Export: BibTeX | EndNote (RIS)      

[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.

URL:     OR

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
[1] 曹博林 . 社交媒体: 概念、发展历程、特征与未来——兼谈当下对社交媒体认识的模糊之处[J]. 湖南广播电视大学学报, 2011(3):65-69.
[1] ( Cao Bolin . Social Media: Definition, History of Development, Features and Future—The Ambiguous Cognition of Social Media[J]. Journal of Hunan Radio & Television University, 2011(3):65-69.)
[2] 雷霞 . 谣言: 概念演变与发展[J]. 新闻与传播研究, 2016(9):113-118.
[2] ( Lei Xia . Rumor: Concept Evolution and Development[J]. Journalism & Communication, 2016(9):113-118.)
[3] Fanti G, Kairouz P, Oh S , et al. Hiding the Rumor Source[J]. IEEE Transactions on Information Theory, 2017,63(10):6679-6713.
[4] Castillo C, Mendoza M, Poblete B . Information Credibility on Twitter [C]// Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India. 2011: 675-684.
[5] Ma J, Gao W, Wei Z , et al. Detect Rumors Using Time Series of Social Context Information on Microblogging Websites [C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia. ACM, 2015: 1751-1754.
[6] Zhao Z, Resnick P, Mei Q . Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts [C]// Proceedings of the 24th International Conference on World Wide Web, Florence, Italy. ACM, 2015: 1395-1405.
[7] 祖坤琳, 赵铭伟, 郭凯 , 等. 新浪微博谣言检测研究[J]. 中文信息学报, 2017,31(3):198-204.
[7] ( Zu Kunlin, Zhao Mingwei, Guo Kai , et al. Research on the Detection of Rumor on Sina Weibo[J]. Journal of Chinese Information Processing, 2017,31(3):198-204.)
[8] Ma J, Gao W, Mitra P , et al. Detecting Rumors from Microblogs with Recurrent Neural Networks [C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, USA. 2016: 3818-3824.
[9] Chen T, Li X, Yin H , et al. Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection [C]// Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2018: 40-52.
[10] Zhang Y, Chen W, Yeo C K , et al. Detecting Rumors on Online Social Networks Using Multi-Layer Autoencoder [C]// Proceedings of the 2017 IEEE Technology & Engineering Management Conference. IEEE, 2017: 437-441.
[11] Chen W, Zhang Y, Yeo C K , et al. Unsupervised Rumor Detection Based on Users’ Behaviors Using Neural Networks[J]. Pattern Recognition Letters, 2018,105:226-233.
[12] 刘雅辉, 靳小龙, 沈华伟 , 等. 社交媒体中的谣言识别研究综述[J]. 计算机学报, 2018,41(7):1536-1558.
[12] ( Liu Yahui, Jin Xiaolong, Shen Huawei , et al. A Survey on Rumor Identification over Social Media[J]. Chinese Journal of Computers, 2018,41(7):1536-1558.)
[13] Zhou J, Xu W . End-to-End Learning of Semantic Role Labeling Using Recurrent Neural Networks [C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015: 1127-1137.
[14] Chen T, Xu R, He Y , et al. Improving Sentiment Analysis via Sentence Type Classification Using BiLSTM-CRF and CNN[J]. Expert Systems with Applications, 2017,72:221-230.
[15] Blitzer J, McDonald R, Pereira F. Domain Adaptation with Structural Correspondence Learning [C]// Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia. ACM, 2006: 120-128.
[16] Borgwardt K M, Gretton A, Rasch M J , et al. Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy[J]. Bioinformatics, 2006,22(14):e49-e57.
[17] Ghifary M, Kleijn W B, Zhang M . Domain Adaptive Neural Networks for Object Recognition [C]// Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence. 2014: 898-904.
[18] Tzeng E, Hoffman J, Zhang N , et al. Deep Domain Confusion: Maximizing for Domain Invariance[OL]. arXiv Preprint, arXiv: 1412. 3474.
[19] Long M, Cao Y, Wang J, et al. Learning Transferable Features with Deep Adaptation Networks [C]// Proceedings of the 32nd International Conference on Machine Learning, Lille, France. 2015: 97-105.
[20] Mou L, Meng Z, Yan R, et al. How Transferable are Neural Networks in NLP Applications? [C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, USA. 2016: 479-489.
[21] Gretton A, Sriperumbudur B, Sejdinovic D, et al. Optimal Kernel Choice for Large-Scale Two-Sample Tests [C]// Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA. 2012: 1205-1213.
[22] Pennington J, Socher R, Manning C D. GloVe: Global Vectors for Word Representation [C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar. 2014: 1532-1543.
[23] Wu K, Yang S, Zhu K Q. False Rumors Detection on Sina Weibo by Propagation Structures [C]// Proceedings of the 31st International Conference on Data Engineering, Seoul, South Korea. IEEE, 2015: 651-662.
[1] Huanrong Shou,Shuqing Deng,Jian Xu. Detecting Online Rumors with Sentiment Analysis[J]. 数据分析与知识发现, 2017, 1(7): 44-51.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938