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research-article

Which cascade is more decisive in rumor detection on social media: : Based on comparison between repost and reply sequences

Authors:

Kan LiuAuthors Info & Claims

Volume 278, Issue C

https://doi.org/10.1016/j.knosys.2023.110857

Published: 25 October 2023 Publication History

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 reposts or replies, to detect rumors. However, most of them view reposts or replies only as input sequences and do not distinguish the two types of sequences and their effects. Hence, this paper proposes a model called “cascade-sequence-based rumor detection” (CSRD) to explore the differences between reposts and replies in rumor detection. The CSRD combines a modified dilated convolution with a bi-directional long short-term memory (Bi-LSTM) neural network to extract the local and global features from the replies or reposts. Accordingly, two rumor datasets are collected from Twitter and Weibo. Based on the two datasets, three experiments are conducted with the proposed models: the rumor detection experiment, the attention-weight experiment, and the early detection experiment. The results reveal the differences between the repost and reply sequences in rumor detection. The differences in information density of reposts and replies affect rumor detection, where replies are denser and more decisive than reposts for obtaining a higher detection accuracy. The differences between the local and global features of the reposts and replies lead to differences in the detection accuracy, where the local features substantially impact the detection results more than the global features. Different data exposure levels of reposts and replies at different propagation stages also influence the accuracy of rumor detection.

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 detection.

References

[1]

Wang Q., Yang X., Xi W., Effects of group arguments on rumor belief and transmission in online communities: An information cascade and group polarization perspective, Inf. Manag. 55 (2017),.

[2]

J. Jasser, Dynamics of Misinformation Cascades, in: Companion Proceedings of the 2019 World Wide Web Conference 4, 2019, pp. 33–36, https://doi.org/10.1145/3308560.3314194.

[3]

Difonzo N., Bordia P., Rumor, gossip and urban legends, Diogenes 54 (2007) 19–35,.

[4]

A. Zubiaga, M. Liakata, R. Procter, K. Bontcheva, P. Tolmie, Towards detecting rumours in social media, in: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

[5]

Bikhchandani S., Hirshleifer D., Welch I., A theory of fads, fashion, custom, and cultural change as informational cascades, J. Political Econ. 100 (5) (1992) 992–1026.

[6]

Yang Y., Wang Y., Wang L., Meng J., PostCom2DR: Utilizing information from post and comments to detect rumors, Expert Syst. Appl. 189 (2022),.

Digital Library

[7]

L. Cui, K. Shu, S. Wang, D. Lee, H. Liu, defend: A system for explainable fake news detection, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 2961–2964.

[8]

Zhang H., Qian S., Fang Q., Xu C., Multi-modal meta multi-task learning for social media rumor detection, IEEE Trans. Multimed. 24 (2022) 1449–1459,.

Digital Library

[9]

Wang W., Qiu Y., Xuan S., Yang W., Liu L., Early rumor detection based on deep recurrent Q-learning, Secur. Commun. Netw. 2021 (2021),.

Digital Library

[10]

J. Ma, W. Gao, K.-F. Wong, Rumor Detection on Twitter with Tree-structured Recursive Neural Networks, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018, pp. 1980–1989, https://doi.org/10.18653/v1/P18-1184.

[11]

S. Kumar, K. Carley, Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 5047–5058, https://doi.org/10.18653/v1/P19-1498.

[12]

T. Bian, X. Xiao, T. Xu, P. Zhao, W. Huang, Y. Rong, J. Huang, Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 549–556, https://doi.org/10.1609/aaai.v34i01.5393.

[13]

Jiang T., Hou Y., Wang Q., Does micro-blogging make us “shallow”? Sharing information online interferes with information comprehension, Comput. Hum. Behav. 59 (2016) 210–214,.

Digital Library

[14]

Shu K., Sliva A., Wang S., Tang J., Liu H., Fake news detection on social media: A data mining perspective, SIGKDD Explor. Newsl. 19 (1) (2017) 22–36,.

Digital Library

[15]

C. Castillo, M. Mendoza, B. Poblete, Information Credibility on Twitter, in: Proceedings of the 20th International Conference on World Wide Web, 2011, pp. 675–684, https://doi.org/10.1145/1963405.1963500.

[16]

Sailunaz K., Kawash J., Alhajj R., Tweet and user validation with supervised feature ranking and rumor classification, Multimedia Tools Appl. (2022) 1–21,.

Digital Library

[17]

S. Kwon, M. Cha, K. Jung, W. Chen, Y. Wang, Prominent features of rumor propagation in online social media, in: 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1103–1108, https://doi.org/10.1109/ICDM.2013.61.

[18]

T. Manjunath Kumar, R. Murugeswari, D. Devaraj, J. Hemalatha, Comparison of deep learning and random forest for rumor identification in social networks, in: International Conference on Innovative Computing and Communications, 2020, pp. 133–146, https://doi.org/10.1145/3508230.3508246.

[19]

F. Yang, Y. Liu, X. Yu, M. Yang, Automatic Detection of Rumor on Sina Weibo, in: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, 2012, https://doi.org/10.1145/2350190.2350203.

[20]

J. Ma, W. Gao, Z. Wei, Y. Lu, K.-F. Wong, 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, 2015, pp. 1751–1754, https://doi.org/10.1145/2806416.2806607.

[21]

Wang S., Li Z., Wang Y., Zhang Q., Machine learning methods to predict social media disaster rumor refuters, Int. J. Environ. Res. Public Health 16 (8) (2019) 1452,.

[22]

V. Qazvinian, E. Rosengren, D. Radev, Q. Mei, Rumor has it: Identifying misinformation in microblogs, in: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 2011, pp. 1589–1599, https://doi.org/10.5555/2145432.2145602.

[23]

Dong X., Lian Y., Chi Y., Tang X., Liu Y., A two-step rumor detection model based on the supernetwork theory about weibo, J. Supercomput. 77 (10) (2021) 12050–12074,.

Digital Library

[24]

Vosoughi S., Mohsenvand M.N., Roy D., Rumor gauge: Predicting the veracity of rumors on Twitter, Acm Trans. Knowl. Discov. Data 11 (4) (2017),.

Digital Library

[25]

G. Liang, J. Yang, C. Xu, Automatic rumors identification on Sina Weibo, in: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016, pp. 1523–1531, https://doi.org/10.1109/FSKD.2016.7603402.

[26]

F. Yu, Q. Liu, S. Wu, L. Wang, T. Tan, A Convolutional Approach for Misinformation Identification, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, 2017, pp. 3901–3907, https://doi.org/10.24963/ijcai.2017/545.

[27]

Yu F., Liu Q., Wu S., Wang L., Tan T., Attention-based convolutional approach for misinformation identification from massive and noisy microblog posts, Comput. Secur. 83 (C) (2019) 106–121,.

Digital Library

[28]

Tu K., Chen C., Hou C., Yuan J., Li J., Yuan X., Rumor2vec: A rumor detection framework with joint text and propagation structure representation learning, Inform. Sci. 560 (2021) 137–151,.

[29]

J. Ma, W. Gao, P. Mitra, S. Kwon, B.J. Jansen, K.-F. Wong, M. Cha, Detecting Rumors from Microblogs with Recurrent Neural Networks, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016, pp. 3818–3824, https://doi.org/10.5555/3061053.3061153.

[30]

Chen T., Li X., Yin H., Zhang J., Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection, in: Trends and Applications in Knowledge Discovery and Data Mining, 2018, pp. 40–52,.

[31]

Alkhodair S.A., Ding S.H., Fung B.C., Liu J., Detecting breaking news rumors of emerging topics in social media, Inf. Process. Manage. 57 (2) (2020),.

Digital Library

[32]

X. Zhang, J. Cao, X. Li, Q. Sheng, L. Zhong, K. Shu, Mining Dual Emotion for Fake News Detection, in: Proceedings of the Web Conference 2021, 2021, pp. 3465–3476, https://doi.org/10.1145/3442381.3450004.

[33]

Y. Liu, Y.-F. Wu, Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 2018, https://doi.org/10.1609/aaai.v32i1.11268.

[34]

W.Y. Wang, “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017, pp. 422–426, https://doi.org/10.18653/v1/P17-2067.

[35]

O. Ajao, D. Bhowmik, S. Zargari, Fake News Identification on Twitter with Hybrid CNN and RNN Models, in: Proceedings of the 9th International Conference on Social Media and Society, 2018, pp. 226–230, https://doi.org/10.1145/3217804.3217917.

[36]

Lv S., Zhang H., He H., Chen B., Microblog rumor detection based on comment sentiment and CNN-LSTM, in: Artificial Intelligence in China, 2020, pp. 148–156,.

[37]

J. Ma, W. Gao, K.-F. Wong, Rumor Detection on Twitter with Tree-structured Recursive Neural Networks, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018, pp. 1980–1989, https://doi.org/10.18653/v1/P18-1184.

[38]

J. Ma, W. Gao, K.-F. Wong, Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning, in: The World Wide Web Conference, 2019, pp. 3049–3055, https://doi.org/10.1145/3308558.3313741.

[39]

Xu D., Liu Q., Zhu L., Tan Z., Gao F., Zhao J., GCNRDM: A social network rumor detection method based on graph convolutional network in mobile computing, Wirel. Commun. Mob. Comput. (2021),.

Digital Library

[40]

J. Chen, J. She, An Analysis of Verifications in Microblogging Social Networks – Sina Weibo, in: 2012 32nd International Conference on Distributed Computing Systems Workshops, 2012, pp. 147–154, https://doi.org/10.1109/ICDCSW.2012.68.

[41]

J. Kim, J. Yoo, Role of Sentiment in Message Propagation: Reply vs. Retweet Behavior in Political Communication, in: 2012 International Conference on Social Informatics, 2012, pp. 131–136, https://doi.org/10.1109/SocialInformatics.2012.33.

[42]

Z. Chen, J. Yang, J.H. Wang, A cascading framework for uncovering spammers in social networks, in: 2014 IFIP Networking Conference, 2014, pp. 1–9, https://doi.org/10.1109/IFIPNetworking.2014.6857080.

[43]

Borondo J., Morales A., Benito R., Losada J., Multiple leaders on a multilayer social media, Chaos Solitons Fractals 72 (2015) 90–98,.

[44]

Xiong X., Li Y., Qiao S., Han N., Wu Y., Peng J., Li B., An emotional contagion model for heterogeneous social media with multiple behaviors, Physica A 490 (2018) 185–202,.

[45]

K. Wu, S. Yang, K.Q. Zhu, False rumors detection on Sina Weibo by propagation structures, in: 2015 IEEE 31st International Conference on Data Engineering, 2015, pp. 651–662, https://doi.org/10.1109/ICDE.2015.7113322.

[46]

Luo Y., Ma J., Yeo C.K., BCMM: A novel post-based augmentation representation for early rumour detection on social media, Pattern Recognit. 113 (2021),.

[47]

Kim D., Seo D., Cho S., Kang P., Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec, Inform. Sci. 477 (2019) 15–29,.

[48]

Liu R., Cai W., Li G., Ning X., Jiang Y., Hybrid dilated convolution guided feature filtering and enhancement strategy for hyperspectral image classification, IEEE Geosci. Remote Sens. Lett. 19 (2021) 1–5,.

[49]

Roy R.S., Quadir F., Soltanikazemi E., Cheng J., A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers, Bioinformatics 38 (7) (2022) 1904–1910,.

[50]

Ma Y.-J., Shuai H.-H., Cheng W.-H., Spatiotemporal dilated convolution with uncertain matching for video-based crowd estimation, IEEE Trans. Multimed. 24 (2021) 261–273,.

Digital Library

[51]

Mariani S., Rendu Q., Urbani M., Sbarufatti C., Causal dilated convolutional neural networks for automatic inspection of ultrasonic signals in non-destructive evaluation and structural health monitoring, Mech. Syst. Signal Process. 157 (2021),.

[52]

C. Lea, R. Vidal, A. Reiter, G.D. Hager, Temporal convolutional networks: A unified approach to action segmentation, in: European Conference on Computer Vision, 2016, pp. 47–54.

[53]

P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, G. Cottrell, Understanding convolution for semantic segmentation, in: 2018 IEEE Winter Conference on Applications of Computer Vision, 2018, pp. 1451–1460, https://doi.org/10.1109/WACV.2018.00163.

[54]

K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778, https://doi.org/10.1109/CVPR.2016.90.

[55]

P. Zhou, W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, B. Xu, Attention-based bidirectional long short-term memory networks for relation classification, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016, pp. 207–212.

[56]

Yang C., Zhou X., Zafarani R., CHECKED: Chinese COVID-19 fake news dataset, Soc. Netw. Anal. Min. 11 (1) (2021) 1–8.

[57]

K. Sharma, E. Ferrara, Y. Liu, Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 3755–3764.

[58]

Kim Y., Convolutional neural networks for sentence classification, 2014, arXiv preprint arXiv:1408.5882.

[59]

Liu P., Qiu X., Huang X., Recurrent neural network for text classification with multi-task learning, 2016, arXiv preprint arXiv:1605.05101.

[60]

Tu K., Chen C., Hou C., Yuan J., Li J., Yuan X., Rumor2vec: a rumor detection framework with joint text and propagation structure representation learning, Inform. Sci. 560 (2021) 137–151,.

[61]

L.M.S. Khoo, H.L. Chieu, Z. Qian, J. Jiang, Interpretable rumor detection in microblogs by attending to user interactions, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 8783–8790.

[62]

Pröllochs N., Feuerriegel S., Mechanisms of true and false rumor sharing in social media: Wisdom-of-crowds or herd behavior?, 2022, arXiv preprint arXiv:2207.03020.

[63]

Song C., Yang C., Chen H., Tu C., Liu Z., Sun M., CED: credible early detection of social media rumors, IEEE Trans. Knowl. Data Eng. 33 (8) (2019) 3035–3047.

Cited By

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
Ji CLuo NWu YFang JSong SLi XHuang YXie D(2024)Joint Detection of Rumors and Stances Based on Integrating Temporal and Structural InformationProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675517(605-612)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675517
Tan XMao SXiao XYang Y(2024)Explainable rumor detection based on grey clusteringInformation Sciences: an International Journal10.1016/j.ins.2024.121055679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121055

Index Terms

Which cascade is more decisive in rumor detection on social media: Based on comparison between repost and reply sequences

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 278, Issue C

Oct 2023

1022 pages

ISSN:0950-7051

Issue’s Table of Contents

Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 25 October 2023

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Cited By

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
Ji CLuo NWu YFang JSong SLi XHuang YXie D(2024)Joint Detection of Rumors and Stances Based on Integrating Temporal and Structural InformationProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence10.1145/3675417.3675517(605-612)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3675417.3675517
Tan XMao SXiao XYang Y(2024)Explainable rumor detection based on grey clusteringInformation Sciences: an International Journal10.1016/j.ins.2024.121055679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121055

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