More Web Proxy on the site http://driver.im/

research-article

Deep spatial–temporal structure learning for rumor detection on Twitter

Authors:

Bin WangAuthors Info & Claims

Neural Computing and Applications, Volume 35, Issue 18

Pages 12995 - 13005

https://doi.org/10.1007/s00521-020-05236-4

Published: 08 August 2020 Publication History

Abstract

The widespread of rumors on social media, carrying unreal or even malicious information, brings negative effects on society and individuals, which makes the automatic detection of rumors become particularly important. Most of the previous studies focused on text mining using supervised models based on feature engineering or deep learning models. In recent years, another parallel line of works, which focuses on the spatial structure of message propagation, provides an alternative and promising solution. However, these existing methods in this parallel line largely overlooked the temporal structure information associated with the spatial structure in message propagation. Actually the addition of temporal structure information can make the message propagations be classified from the perspective of spatial–temporal structure, a more fine-grained perspective. Under these observations, this paper proposes a spatial–temporal structure neural network for rumor detection, termed as STS-NN, which treats the spatial structure and the temporal structure as a whole to model the message propagation. All the STS-NN units are parameter shared and consist of three components, including spatial capturer, temporal capturer and integrator, to capture the spatial–temporal information for the message propagation. The results show that our approach obtains better performance than baselines in both rumor classification and early detection.

References

[1]

DiFonzo N and Bordia P Rumor psychology: social and organizational approaches 2007 Washington American Psychological Association

[2]

Jin Z, Cao J, Guo H, Zhang Y, Wang Y, Luo J (2017) Detection and analysis of 2016 us presidential election related rumors on twitter. In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, pp 14–24

[3]

Ma J, Gao W, Wong KF (2018) Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 1980–1989

[4]

Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on World wide web, pp 675–684

[5]

Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining. IEEE, pp 1103–1108

[6]

Liu X, Nourbakhsh A, Li Q, Fang R, Shah S (2015) Real-time rumor debunking on twitter. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 1867–1870

[7]

Ma J, Gao W, Wei Z, Lu Y, Wong KF (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 1751–1754

[8]

Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 3818–3824

[9]

Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806

[10]

Wu K, Yang S, Zhu KQ (2015) False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st international conference on data engineering. IEEE, pp 651–662

[11]

Ma J, Gao W, Wong KF (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 708–717

[12]

Zubiaga A, Liakata M, Procter R, Hoi GWS, and Tolmie P Analysing how people orient to and spread rumours in social media by looking at conversational threads PLoS ONE 2016 11 3 0150989

[13]

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

[14]

Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1724–1734

[15]

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

[16]

Liu Y, Wu YFB (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Thirty-second AAAI conference on artificial intelligence

[17]

Yuan C, Ma Q, Zhou W, Han J, Hu S (2019) Jointly embedding the local and global relations of heterogeneous graph for rumor detection. arXiv:190904465

[18]

Zhao Z, Resnick P, Mei Q (2015) Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th international conference on world wide web, pp 1395–1405

[19]

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:14126980

[20]

Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD workshop on mining data semantics, p 13

[21]

Huang Q, Zhou C, Wu J, Wang M, Wang B (2019) Deep structure learning for rumor detection on twitter. In: 2019 international joint conference on neural networks (IJCNN). IEEE, pp 1–8

Cited By

Pan LLi QYu L(2024)GMRDInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.34865917:1(1-17)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJITSA.348659
Yang CYu XWu JXue DDuan Y(2024)RumorSAGE: Semantic Augmentation Graph for Early Rumor DetectionProceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy10.1145/3672919.3672994(406-412)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3672919.3672994
Cheung TLam K(2023)Causal diffused graph-transformer network with stacked early classification loss for efficient stream classification of rumoursKnowledge-Based Systems10.1016/j.knosys.2023.110807277:COnline publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110807
Show More Cited By

Recommendations

Automatic detection of rumor on Sina Weibo
MDS '12: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics

The problem of gauging information credibility on social networks has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift ...
Preventing rumor spread with deep learning
Abstract
The spreading of false rumors through online media is a serious social problem. The current process of fact-checking mostly relies on the responses of crowds or journalists to perform investigations, which can be performed after a ...
Highlights
- Using only a claim sentence of a rumor to identify false rumor.
- The proposed ...
Which cascade is more decisive in rumor detection on social media: Based on comparison between repost and reply sequences
Abstract
Rumor detection research is widely carried out to control the negative impact of rumor spreading. Many researchers conduct their research based on data from social media and prefer to employ different information cascades on social media, such as ...
Highlights
- Proposing CSRD, a rumor detection model, using modified dilated convolution.
- Revealing feature differences in rumor detection between reposts and replies
- Considering detection deadlines’ impact on data exposure levels in early ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 35, Issue 18

Jun 2023

742 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© Springer-Verlag London Ltd., part of Springer Nature 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 August 2020

Accepted: 24 July 2020

Received: 09 May 2020

Author Tags

Qualifiers

Research-article

Funding Sources

the NSFC
the ARC DECRA
the Youth Innovation Promotion Association CAS

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pan LLi QYu L(2024)GMRDInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.34865917:1(1-17)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJITSA.348659
Yang CYu XWu JXue DDuan Y(2024)RumorSAGE: Semantic Augmentation Graph for Early Rumor DetectionProceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy10.1145/3672919.3672994(406-412)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1145/3672919.3672994
Cheung TLam K(2023)Causal diffused graph-transformer network with stacked early classification loss for efficient stream classification of rumoursKnowledge-Based Systems10.1016/j.knosys.2023.110807277:COnline publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110807
Gao YWang XHe XFeng HZhang Y(2022)Rumor detection with self-supervised learning on texts and social graphFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-1531-917:4Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1007/s11704-022-1531-9

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents