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

research-article

Unsupervised Rumor Detection Based on Propagation Tree VAE

Authors:

Tao LiAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 35, Issue 10

Pages 10309 - 10323

https://doi.org/10.1109/TKDE.2023.3267821

Published: 01 October 2023 Publication History

Abstract

The wide spread of rumors inflicts damages on social media platforms. Detecting rumors has become an emerging problem concerning the public and government. A crucial problem for rumors detection on social media is the lack of reliably pre-annotated dataset to train classification models. To solve this problem, we propose an unsupervised model that detects rumors by measuring how well the tweets follow the normal patterns. However, the problem is challenging in how to automatically discover the normal patterns of tweets. To tackle the challenge, we first propose a novel tree variational autoencoder model that reconstructs the sentiment labels along the propagation tree of a factual tweet. Then we propose a cross-alignment method to align the multiple modalities, i.e., tree structure and propagation features, and output the final prediction results. We conduct extensive experiments on a real-world dataset collected from Weibo. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised methods and adapts better to the concept drift than state-of-the-art supervised methods.

References

[1]

Z. Zhao, P. Resnick, and Q. Mei, “Enquiring minds: Early detection of rumors in social media from enquiry posts,” in Proc. 24th Int. Conf. World Wide Web, 2015, pp. 1395–1405.

[2]

K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explorations Newslett., vol. 19, no. 1, pp. 22–36, 2017.

Digital Library

[3]

X. Zhou, R. Zafarani, K. Shu, and H. Liu, “Fake news: Fundamental theories, detection strategies and challenges,” in Proc. 12th ACM Int. Conf. Web Search Data Mining, 2019, pp. 836–837.

[4]

F. B. Gereme and W. Zhu, “Early detection of fake news “before it flies high”,” in Proc. 2nd Int. Conf. Big Data Technol., 2019, pp. 142–148.

[5]

W. Ferreira and A. Vlachos, “Emergent: A novel data-set for stance classification,” in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol., 2016, pp. 1163–1168.

[6]

A. Gupta, P. Kumaraguru, C. Castillo, and P. Meier, “TweetCred: Real-time credibility assessment of content on Twitter,” in Proc. Int. Conf. Social Informat., Springer, 2014, pp. 228–243.

[7]

V. L. Rubin, Y. Chen, and N. K. Conroy, “Deception detection for news: Three types of fakes,” Proc. Assoc. Inf. Sci. Technol., vol. 52, no. 1, pp. 1–4, 2015.

[8]

J. Ma, W. Gao, Z. Wei, Y. Lu, and K.-F. Wong, “Detect rumors using time series of social context information on microblogging websites,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manage., 2015, pp. 1751–1754.

[9]

Y. Liu and Y.-F. B. Wu, “Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks,” in Proc. 32nd AAAI Conf. Artif. Intell., 2018, Art. no.

[10]

J. Ma et al., “Detecting rumors from microblogs with recurrent neural networks,” in Proc. 25th Int. Joint Conf. Artif. Intell. Int. Joint Conf. Artif. Intell., 2016, pp. 3818–3824.

[11]

H. Guo, J. Cao, Y. Zhang, J. Guo, and J. Li, “Rumor detection with hierarchical social attention network,” in Proc. 27th ACM Int. Conf. Inf. Knowl. Manage., 2018, pp. 943–951.

[12]

C. Carrasco-Farr é, “The fingerprints of misinformation: How deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions,” Humanities Social Sci. Commun., vol. 9, no. 1, pp. 1–18, 2022.

[13]

J. Ma, W. Gao, and K.-F. Wong, “Detect rumors in microblog posts using propagation structure via kernel learning,” in Proc. 55th Annu. Meeting Assoc. Comput. Linguistics, 2017, pp. 708–717.

[14]

S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science, vol. 359, no. 6380, pp. 1146–1151, 2018.

[15]

T. N. Kipf and M. Welling, “Variational graph auto-encoders,” 2016,.

[16]

M. Simonovsky and N. Komodakis, “GraphVAE: Towards generation of small graphs using variational autoencoders,” in Proc. Int. Conf. Artif. Neural Netw., Springer, 2018, pp. 412–422.

[17]

C. Ma and X. Zhang, “GF-VAE: A flow-based variational autoencoder for molecule generation,” in Proc. 30th ACM Int. Conf. Inf. Knowl. Manage., 2021, pp. 1181–1190.

[18]

L. Clarke, “On cayley's formula for counting trees,” J. London Math. Soc., vol. 1, no. 4, pp. 471–474, 1958.

[19]

E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata, “Generalized zero-and few-shot learning via aligned variational autoencoders,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2019, pp. 8247–8255.

[20]

Y. Wang et al., “EANN: Event adversarial neural networks for multi-modal fake news detection,” in Proc. 24th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2018, pp. 849–857.

[21]

A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi, “Faking sandy: Characterizing and identifying fake images on twitter during hurricane sandy,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 729–736.

[22]

N. Hassan, F. Arslan, C. Li, and M. Tremayne, “Toward automated fact-checking: Detecting check-worthy factual claims by claimbuster,” in Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2017, pp. 1803–1812.

[23]

M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, “A stylometric inquiry into hyperpartisan and fake news,” 2017,.

[24]

Y. Wu, P. K. Agarwal, C. Li, J. Yang, and C. Yu, “Toward computational fact-checking,” Proc. VLDB Endowment, vol. 7, no. 7, pp. 589–600, 2014.

[25]

S. Feng, R. Banerjee, and Y. Choi, “Syntactic stylometry for deception detection,” in Proc. 50th Annu. Meeting Assoc. Comput. Linguistics, 2012, pp. 171–175.

[26]

V. L. Rubin and T. Lukoianova, “Truth and deception at the rhetorical structure level,” J. Assoc. Inf. Sci. Technol., vol. 66, no. 5, pp. 905–917, 2015.

[27]

Q. Li, Q. Zhang, and L. Si, “Rumor detection by exploiting user credibility information, attention and multi-task learning,” in Proc. 57th Annu. Meeting Assoc. Comput. Linguistics, 2019, pp. 1173–1179.

[28]

K. Shu, S. Wang, and H. Liu, “Beyond news contents: The role of social context for fake news detection,” in Proc. 12th ACM Int. Conf. Web Search Data Mining, 2019, pp. 312–320.

[29]

J. Ma, W. Gao, and K.-F. Wong, “Rumor detection on twitter with tree-structured recursive neural networks,” in Proc. 56th Annu. Meeting Assoc. Comput. Linguistics, 2018, pp. 1980–1989.

[30]

J. Li, Y. Sujana, and H.-Y. Kao, “Exploiting microblog conversation structures to detect rumors,” in Proc. 28th Int. Conf. Comput. Linguistics, 2020, pp. 5420–5429.

[31]

E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret, and L. de Alfaro, “Some like it hoax: Automated fake news detection in social networks,” 2017,.

[32]

K. Shu, D. Mahudeswaran, S. Wang, and H. Liu, “Hierarchical propagation networks for fake news detection: Investigation and exploitation,” in Proc. Int. AAAI Conf. Web Social Media, 2020, pp. 626–637.

[33]

K. Wu, S. Yang, and K. Q. Zhu, “False rumors detection on Sina Weibo by propagation structures,” in Proc. IEEE 31st Int. Conf. Data Eng., 2015, pp. 651–662.

[34]

T. Bian et al., “Rumor detection on social media with bi-directional graph convolutional networks,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 549–556.

[35]

J. Ma and W. Gao, “Debunking rumors on Twitter with tree transformer,” in Proc. 28th Int. Conf. Comput. Linguistics, 2020, pp. 5455–5466.

[36]

N. Rosenfeld, A. Szanto, and D. C. Parkes, “A kernel of truth: Determining rumor veracity on twitter by diffusion pattern alone,” in Proc. Web Conf., 2020, pp. 1018–1028.

[37]

A. Lao, C. Shi, and Y. Yang, “Rumor detection with field of linear and non-linear propagation,” in Proc. Web Conf., 2021, pp. 3178–3187.

[38]

L. Wei, D. Hu, W. Zhou, Z. Yue, and S. Hu, “Towards propagation uncertainty: Edge-enhanced Bayesian graph convolutional networks for rumor detection,” in Proc. 59th Annu. Meeting Assoc. Comput. Linguistics 11th Int. Joint Conf. Natural Lang. Process., 2021, pp. 3845–3854.

[39]

N. Ruchansky, S. Seo, and Y. Liu, “CSI: A hybrid deep model for fake news detection,” in Proc. ACM Conf. Inf. Knowl. Manage., 2017, pp. 797–806.

[40]

H. Lin, J. Ma, M. Cheng, Z. Yang, L. Chen, and G. Chen, “Rumor detection on twitter with claim-guided hierarchical graph attention networks,” in Proc. Conf. Empir. Methods Natural Lang. Process., 2021, pp. 10035–10047.

[41]

P. Meel and D. K. Vishwakarma, “Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities,” Expert Syst. Appl., vol. 153, 2020, Art. no.

[42]

Z. Jin, J. Cao, Y. Zhang, and J. Luo, “News verification by exploiting conflicting social viewpoints in microblogs,” in Proc. 30th AAAI Conf. Artif. Intell., 2016, pp. 2972–2978.

[43]

M. D. Vicario, W. Quattrociocchi, A. Scala, and F. Zollo, “Polarization and fake news: Early warning of potential misinformation targets,” ACM Trans. Web, vol. 13, no. 2, pp. 1–22, 2019.

Digital Library

[44]

S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, “Prominent features of rumor propagation in online social media,” in Proc. IEEE 13th Int. Conf. Data Mining, 2013, pp. 1103–1108.

[45]

C. Guo, J. Cao, X. Zhang, K. Shu, and M. Yu, “Exploiting emotions for fake news detection on social media,” 2019,.

[46]

S. Yang, K. Shu, S. Wang, R. Gu, F. Wu, and H. Liu, “Unsupervised fake news detection on social media: A generative approach,” in Proc. AAAI Conf. Artif. Intell., 2019, pp. 5644–5651.

[47]

D. Zhang, Y. Wang, and Z. Zhang, “Identifying and quantifying potential super-spreaders in social networks,” Sci. Rep., vol. 9, no. 1, pp. 1–11, 2019.

[48]

F. Morone, B. Min, L. Bo, R. Mari, and H. A. Makse, “Collective influence algorithm to find influencers via optimal percolation in massively large social media,” Sci. Rep., vol. 6, no. 1, pp. 1–11, 2016.

[49]

Q. Liu, M. Allamanis, M. Brockschmidt, and A. Gaunt, “Constrained graph variational autoencoders for molecule design,” in Proc. Adv. Neural Inf. Process. Syst., 2018, pp. 7795–7804.

[50]

W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” 2018,.

[51]

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” 2013,.

[52]

H. Dai, B. Dai, and L. Song, “Discriminative embeddings of latent variable models for structured data,” in Proc. Int. Conf. Mach. Learn., 2016, pp. 2702–2711.

[53]

K. Cho, B. Van Merri ënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” 2014,.

[54]

G. L. Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. L. C. Farias, and A. Aspuru-Guzik, “Objective-reinforced generative adversarial networks (organ) for sequence generation models,” 2017,.

[55]

C. Wan, T. Probst, L. Van Gool, and A. Yao, “Crossing nets: Combining GANs and VAEs with a shared latent space for hand pose estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 680–689.

[56]

J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special Lecture IE, vol. 2, no. 1, pp. 1–18, 2015.

[57]

P. Qi, Y. Zhang, Y. Zhang, J. Bolton, and C. D. Manning, “Stanza: A python natural language processing toolkit for many human languages,” in Proc. 58th Annu. Meeting Assoc. Comput. Linguistics: Syst. Demonstrations, 2020, pp. 101–108.

[58]

H. Tian et al., “SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis,” 2020,.

[59]

B. Zhao, B. I. Rubinstein, J. Gemmell, and J. Han, “A Bayesian approach to discovering truth from conflicting sources for data integration,” Proc. VLDB Endowment, vol. 5, no. 6, pp. 550–561, 2012.

[60]

K. Shu, L. Cui, S. Wang, D. Lee, and H. Liu, “dEFEND: Explainable fake news detection,” in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2019, pp. 395–405.

Cited By

Chen SChen XMiao DZhang HQin XLu P(2025)ADA-UDAExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125487261:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125487
Nan QSheng QCao JZhu YWang DYang GLi J(2025)Exploiting user comments for early detection of fake news prior to users’ commentingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40674-619:10Online publication date: 1-Oct-2025
https://dl.acm.org/doi/10.1007/s11704-024-40674-6
Zeng JLi XMa X(2025)PPTopicPLM: plug-and-play topic-enhanced pre-trained language model for short-text rumor detectionThe Journal of Supercomputing10.1007/s11227-024-06549-081:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06549-0
Show More Cited By

Recommendations

Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Special Issue on KDD 2016 and Regular Papers

The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated ...
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 ...
Using TwitterTrails.com to Investigate Rumor Propagation
CSCW'15 Companion: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing

Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 35, Issue 10

Oct. 2023

1088 pages

ISSN:1041-4347

Issue’s Table of Contents

1041-4347 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 October 2023

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen SChen XMiao DZhang HQin XLu P(2025)ADA-UDAExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125487261:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125487
Nan QSheng QCao JZhu YWang DYang GLi J(2025)Exploiting user comments for early detection of fake news prior to users’ commentingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40674-619:10Online publication date: 1-Oct-2025
https://dl.acm.org/doi/10.1007/s11704-024-40674-6
Zeng JLi XMa X(2025)PPTopicPLM: plug-and-play topic-enhanced pre-trained language model for short-text rumor detectionThe Journal of Supercomputing10.1007/s11227-024-06549-081:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06549-0
Xiao YLi XZhang QLv RLi QWang R(2024)Spreading Mosaic: An Image Restoration-Inspired Social Rumor Propagation ModelIEEE Transactions on Multimedia10.1109/TMM.2023.330509526(2906-2917)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3305095
Li JBin YPeng LYang YLi YJin HHuang Z(2024)Focusing on Relevant Responses for Multi-Modal Rumor DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338969436:11(6225-6236)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3389694
Huynh LKilcullen JHong J(2024)Rumor Alteration for Improving Rumor GenerationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0576-7_26(352-362)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0576-7_26

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents