A Survey on Fake News Detection in Social Media Using Graph Neural Networks
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21539Keywords:
Academic Achievement, Prediction Model, Ordinal Logistic Regression, Artificial Neural Networks ANNs, Multi-Layer Perceptron MLP.Abstract
Nowadays, social media has become the key source of information for anyone seeking about current events across the world. This information may be fake or real news. On social media platforms, fake news negatively impacts politics, the economy, and health, and affects the stability of society. The research on fake news detection has received widespread attention in the field of computer science. There are many effective methods of fake news detection technology including natural language processing (NLP) and machine learning techniques, primarily focusing on content analysis and user behavior. While these methods have shown promise, they often fall short in capturing the complex relational and propagation patterns inherent in social networks. Fake news exhibits distinct features such as misleading headlines, and fabricated content, making its detection challenging. To address these issues, Graph Neural Networks (GNNs) have been introduced as a superior solution. GNNs are particularly effective in processing graph-structured data, allowing them to model the intricate connections and dissemination patterns of news in social networks more accurately. This study provides an overview A variety of false information and their characteristics and discusses various techniques and features used in fake news detection. As well as advanced GNN-based techniques and datasets used to implement practical fake news detection systems from multiple perspectives and future research directions. In addition, tables and summary figures help researchers understand the full picture of fake news detection. Finally, the object of this review is to help other researchers improve fake news detection models using GNNs.
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References
B. Rath, X. Morales, and J. Srivastava, “SCARLET: explainable attention based graph neural network for fake news spreader prediction,” in Pacific-Asia conference on knowledge discovery and data mining, Springer, 2021, pp. 714–727.
K. B. Sathyanarayana, S. Ahmed, and H. K. Pradeep, “A Hybrid GNN Model for Fake News Detection in Digital Media,” 2023.
J. Zhang, B. Dong, and S. Y. Philip, “Fakedetector: Effective fake news detection with deep diffusive neural network,” in 2020 IEEE 36th international conference on data engineering (ICDE), IEEE, 2020, pp. 1826–1829.
A. Habib, M. Z. Asghar, A. Khan, A. Habib, and A. Khan, “False information detection in online content and its role in decision making: a systematic literature review,” Soc. Netw. Anal. Min., vol. 9, pp. 1–20, 2019.
K. Shu, S. Wang, and H. Liu, “Beyond news contents: The role of social context for fake news detection,” in Proceedings of the twelfth ACM international conference on web search and data mining, 2019, pp. 312–320.
W. Xu, J. Wu, Q. Liu, S. Wu, and L. Wang, “Evidence-aware fake news detection with graph neural networks,” in Proceedings of the ACM Web Conference 2022, 2022, pp. 2501–2510.
A. Bovet and H. A. Makse, “Influence of fake news in Twitter during the 2016 US presidential election,” Nat. Commun., vol. 10, no. 1, p. 7, 2019.
I. A. Pilkevych, D. L. Fedorchuk, M. P. Romanchuk, and O. M. Naumchak, “Approach to the fake news detection using the graph neural networks,” J. Edge Comput., vol. 2, no. 1, pp. 24–36, 2023.
I. A. Pilkevych, D. L. Fedorchuk, M. P. Romanchuk, and O. M. Naumchak, “An analysis of approach to the fake news assessment based on the graph neural networks,” in CEUR Workshop Proceedings, 2023, pp. 56–65.
M. Goksu and N. Cavus, “Fake news detection on social networks with artificial intelligence tools: systematic literature review,” in International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, Springer, 2019, pp. 47–53.
Y. M. Rocha, G. A. de Moura, G. A. Desidério, C. H. de Oliveira, F. D. Lourenço, and L. D. de Figueiredo Nicolete, “The impact of fake news on social media and its influence on health during the COVID-19 pandemic: A systematic review,” J. Public Health (Bangkok)., pp. 1–10, 2021.
Z. N. S. weli, “Covid-19 Prediction Model Using Data Mining Algorithms,” Al-Mustansiriyah J. Sci., vol. 33, no. 1, pp. 45–50, 2022, [Online]. Available: https://mjs.uomustansiriyah.edu.iq/index.php/MJS/article/view/1076
D. Orso, N. Federici, R. Copetti, L. Vetrugno, and T. Bove, “Infodemic and the spread of fake news in the COVID-19-era,” Eur. J. Emerg. Med., 2020.
S. Rode-Hasinger, A. Kruspe, and X. X. Zhu, “True or false? Detecting false information on social media using graph neural networks,” in Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), 2022, pp. 222–229.
S. Chandra, P. Mishra, H. Yannakoudakis, M. Nimishakavi, M. Saeidi, and E. Shutova, “Graph-based modeling of online communities for fake news detection,” arXiv Prepr. arXiv2008.06274, 2020.
S. J. Muhamed, “Detection and Prevention WEB-Service for Fraudulent E-Transaction using APRIORI and SVM,” Al-Mustansiriyah J. Sci., vol. 33, no. 4, pp. 72–79, 2022.
S. Imaduwage, P. Kumara, and W. J. Samaraweera, “Capturing Credibility of Users for an Efficient Propagation Network Based Fake News Detection,” in 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), IEEE, 2022, pp. 212–217.
K. Soga, S. Yoshida, and M. Muneyasu, “Exploiting stance similarity and graph neural networks for fake news detection,” Pattern Recognit. Lett., vol. 177, pp. 26–32, 2024.
F. B. Mahmud, M. M. S. Rayhan, M. H. Shuvo, I. Sadia, and M. K. Morol, “A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection,” in 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), IEEE, 2022, pp. 97–102.
M. R. Islam, S. Liu, X. Wang, and G. Xu, “Deep learning for misinformation detection on online social networks: a survey and new perspectives,” Soc. Netw. Anal. Min., vol. 10, pp. 1–20, 2020.
S. A. Alkhodair, S. H. H. Ding, B. C. M. Fung, and J. Liu, “Detecting breaking news rumors of emerging topics in social media,” Inf. Process. Manag., vol. 57, no. 2, p. 102018, 2020.
B. Rath, W. Gao, J. Ma, and J. Srivastava, “Utilizing computational trust to identify rumor spreaders on Twitter,” Soc. Netw. Anal. Min., vol. 8, pp. 1–16, 2018.
A. Aldayel and W. Magdy, “Your stance is exposed! analysing possible factors for stance detection on social media,” Proc. ACM Human-Computer Interact., vol. 3, no. CSCW, pp. 1–20, 2019.
S. Kaur, P. Kumar, and P. Kumaraguru, “Automating fake news detection system using multi-level voting model,” Soft Comput., vol. 24, no. 12, pp. 9049–9069, 2020.
X. Zhou, A. Jain, V. V Phoha, and R. Zafarani, “Fake news early detection: A theory-driven model,” Digit. Threat. Res. Pract., vol. 1, no. 2, pp. 1–25, 2020.
S. K. Hamed, M. J. Ab Aziz, and M. R. Yaakub, “A Review of Fake News Detection Models: Highlighting the Factors Affecting Model Performance and the Prominent Techniques Used,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 7, 2023.
C. Paul and M. Matthews, “The Russian ‘firehose of falsehood’ propaganda model,” Rand Corp., vol. 2, no. 7, pp. 1–10, 2016.
K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explor. Newsl., vol. 19, no. 1, pp. 22–36, 2017.
X. Zhou and R. Zafarani, “A survey of fake news: Fundamental theories, detection methods, and opportunities,” ACM Comput. Surv., vol. 53, no. 5, pp. 1–40, 2020.
K. Stahl, “Fake news detection in social media,” Calif. State Univ. Stanislaus, vol. 6, pp. 4–15, 2018.
D. M. J. Lazer et al., “The science of fake news,” Science (80-. )., vol. 359, no. 6380, pp. 1094–1096, 2018.
E. C. Tandoc Jr, Z. W. Lim, and R. Ling, “Defining ‘fake news’ A typology of scholarly definitions,” Digit. Journal., vol. 6, no. 2, pp. 137–153, 2018.
C. Wardle, “Fake news. It’s complicated,” First Draft, vol. 16, pp. 1–11, 2017.
N. R. Hanson, “A note on statements of fact,” Analysis, vol. 13, no. 1, p. 24, 1952.
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, p. 112986, 2020.
B. Lakzaei, M. Haghir Chehreghani, and A. Bagheri, “Disinformation detection using graph neural networks: a survey,” Artif. Intell. Rev., vol. 57, no. 3, p. 52, 2024.
A. Bondielli and F. Marcelloni, “A survey on fake news and rumour detection techniques,” Inf. Sci. (Ny)., vol. 497, pp. 38–55, 2019.
Y. Yu, “Review of the Application of Machine Learning in Rumor Detection,” ACM International Conference Proceeding Series. pp. 46–52, 2021. doi: 10.1145/3448218.3448238.
Y. Wang, S. Qian, J. Hu, Q. Fang, and C. Xu, “Fake news detection via knowledge-driven multimodal graph convolutional networks,” ICMR 2020 - Proc. 2020 Int. Conf. Multimed. Retr., pp. 540–547, 2020, doi: 10.1145/3372278.3390713.
A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, “Detection and resolution of rumours in social media: A survey,” ACM Comput. Surv., vol. 51, no. 2, 2018, doi: 10.1145/3161603.
F. Pierri and S. Ceri, “False news on social media: A data-driven survey,” SIGMOD Rec., vol. 48, no. 2, pp. 18–32, 2019, doi: 10.1145/3377330.3377334.
H. T. Phan, N. T. Nguyen, and D. Hwang, “Fake news detection: A survey of graph neural network methods,” Appl. Soft Comput., p. 110235, 2023.
N. Sitaula, C. K. Mohan, J. Grygiel, X. Zhou, and R. Zafarani, “Credibility-Based Fake News Detection,” pp. 163–182, 2020, doi: 10.1007/978-3-030-42699-6_9.
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proc. IEEE, vol. 104, no. 1, pp. 11–33, 2016, doi: 10.1109/JPROC.2015.2483592.
M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, “A stylometric inquiry into hyperpartisan and fake news,” ACL 2018 - 56th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap., vol. 1, pp. 231–240, 2018, doi: 10.18653/v1/p18-1022.
A. L. Ginsca, A. Popescu, and M. Lupu, “Credibility in information retrieval,” Found. Trends Inf. Retr., vol. 9, no. 5, pp. 355–475, 2015, doi: 10.1561/1500000046.
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,” CEUR Workshop Proc., vol. 1960, pp. 1–12, 2017.
Z. Wei et al., “An empirical study on uncertainty identification in social media context,” Soc. Media Content Anal. Nat. Lang. Process. Beyond, no. Acl, pp. 79–88, 2017, doi: 10.1142/9789813223615_0007.
N. Ruchansky, S. Seo, and Y. Liu, “CSI: A hybrid deep model for fake news detection,” Int. Conf. Inf. Knowl. Manag. Proc., vol. Part F1318, pp. 797–806, 2017, doi: 10.1145/3132847.3132877.
K. Shu, D. Mahudeswaran, S. Wang, and H. Liu, “Hierarchical propagation networks for fake news detection: Investigation and exploitation,” Proc. 14th Int. AAAI Conf. Web Soc. Media, ICWSM 2020, no. Icwsm, pp. 626–637, 2020, doi: 10.1609/icwsm.v14i1.7329.
L. Wu and H. Liu, “Tracing fake-news footprints: Characterizing social media messages by how they propagate,” WSDM 2018 - Proc. 11th ACM Int. Conf. Web Search Data Min., vol. 2018-Febua, pp. 637–645, 2018, doi: 10.1145/3159652.3159677.
A. Paraschiv, G. E. Zaharia, D. C. Cercel, and M. Dascalu, “Graph convolutional networks applied to fakenews: Corona virus and 5g conspiracy,” UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci., vol. 83, no. 2, pp. 71–82, 2021.
L. Zhang, J. Li, B. Zhou, and Y. Jia, “Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks,” Mach. Learn. Knowl. Extr., vol. 3, no. 1, pp. 84–94, 2021, doi: 10.3390/make3010005.
W. Gao, K.-F. Wong, J. ; Ma, W. ; Gao, and J. Ma, “Rumor detection on Twitter with tree-structured recursive neural Rumor detection on Twitter with tree-structured recursive neural networks networks Jing MA Citation Citation Rumor Detection on Twitter with Tree-structured Recursive Neural Networks,” no. Acl, pp. 1980–1989, 2018, [Online]. Available: https://ink.library.smu.edu.sg/sis_research
B. Koloski, T. Stepišnik Perdih, M. Robnik-Šikonja, S. Pollak, and B. Škrlj, “Knowledge graph informed fake news classification via heterogeneous representation ensembles,” Neurocomputing, vol. 496, pp. 208–226, 2022, doi: 10.1016/j.neucom.2022.01.096.
D. T. Vu and J. J. Jung, “Rumor detection by propagation embedding based on graph convolutional network,” Int. J. Comput. Intell. Syst., vol. 14, no. 1, pp. 1053–1065, 2021, doi: 10.2991/ijcis.d.210304.002.
Y. J. Lu and C. Te Li, “GCAN: Graph-aware co-attention networks for explainable fake news detection on social media,” Proc. Annu. Meet. Assoc. Comput. Linguist., pp. 505–514, 2020, doi: 10.18653/v1/2020.acl-main.48.
Z. Ke, Z. Li, C. Zhou, J. Sheng, W. Silamu, and Q. Guo, “Rumor detection on social media via fused semantic information and a propagation heterogeneous graph,” Symmetry (Basel)., vol. 12, no. 11, pp. 1–14, 2020, doi: 10.3390/sym12111806.
K. Sharma, F. Qian, H. Jiang, N. Ruchansky, M. Zhang, and Y. Liu, “Combating fake news: A survey on identification and mitigation techniques,” ACM Trans. Intell. Syst. Technol., vol. 10, no. 3, 2019, doi: 10.1145/3305260.
E. Aïmeur, S. Amri, and G. Brassard, Fake news, disinformation and misinformation in social media: a review, vol. 13, no. 1. Springer Vienna, 2023. doi: 10.1007/s13278-023-01028-5.
K. M. Yazdi, A. M. Yazdi, S. Khodayi, J. Hou, W. Zhou, and S. Saedy, “Yazdi Svm,” vol. 14, no. 2, pp. 38–42, 2020.
X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf. Process. Manag., vol. 57, no. 2, p. 102025, 2020, doi: 10.1016/j.ipm.2019.03.004.
K. Shu, X. Zhou, S. Wang, R. Zafarani, and H. Liu, “The role of user profiles for fake news detection,” in Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, 2019, pp. 436–439.
S. Jiang, X. Chen, L. Zhang, S. Chen, and H. Liu, “User-Characteristic Enhanced Model for Fake News Detection in Social Media,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11838 LNAI, pp. 634–646, 2019, doi: 10.1007/978-3-030-32233-5_49.
M. Cardaioli, S. Cecconello, M. Conti, L. Pajola, and F. Turrin, “Fake News Spreaders Profiling through Behavioural Analysis Notebook for PAN at CLEF 2020,” CEUR Workshop Proc., vol. 2696, no. September, pp. 22–25, 2020.
S. K. Uppada, K. Manasa, B. Vidhathri, R. Harini, and B. Sivaselvan, “Novel approaches to fake news and fake account detection in OSNs: user social engagement and visual content centric model,” Soc. Netw. Anal. Min., vol. 12, no. 1, pp. 1–19, 2022, doi: 10.1007/s13278-022-00878-9.
Q. Zhang, S. Liang, A. Lipani, and E. Yilmaz, “Reply-aided detection of misinformation via Bayesian deep learning,” Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019, pp. 2333–2343, 2019, doi: 10.1145/3308558.3313718.
Y. Liu and Y. F. B. Wu, “Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks,” 32nd AAAI Conf. Artif. Intell. AAAI 2018, pp. 354–361, 2018, doi: 10.1609/aaai.v32i1.11268.
R. Mishra, “Fake news detection using higher-order user to user mutual-attention progression in propagation paths,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2020-June, pp. 2775–2783, 2020, doi: 10.1109/CVPRW50498.2020.00334.
L. Wu and Y. Rao, “Adaptive interaction fusion networks for fake news detection,” Front. Artif. Intell. Appl., vol. 325, pp. 2220–2227, 2020, doi: 10.3233/FAIA200348.
M. Madani, H. Motameni, and R. Roshani, “Fake News Detection Using Feature Extraction, Natural Language Processing, Curriculum Learning, and Deep Learning,” Int. J. Inf. Technol. Decis. Mak., pp. 1–36, 2023.
S. Asghari, M. H. Chehreghani, and M. H. Chehreghani, “On Using Node Indices and Their Correlations for Fake Account Detection,” in 2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022, pp. 5656–5661.
Y. Han, S. Karunasekera, and C. Leckie, “Graph neural networks with continual learning for fake news detection from social media,” arXiv Prepr. arXiv2007.03316, 2020.
Y. Ren, B. Wang, J. Zhang, and Y. Chang, “Adversarial active learning based heterogeneous graph neural network for fake news detection,” in 2020 IEEE International Conference on Data Mining (ICDM), IEEE, 2020, pp. 452–461.
S. C. R. Gangireddy, D. P, C. Long, and T. Chakraborty, “Unsupervised fake news detection: A graph-based approach,” in Proceedings of the 31st ACM conference on hypertext and social media, 2020, pp. 75–83.
V.-H. Nguyen, K. Sugiyama, P. Nakov, and M.-Y. Kan, “Fang: Leveraging social context for fake news detection using graph representation,” in Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 1165–1174.
C. Song, K. Shu, and B. Wu, “Temporally evolving graph neural network for fake news detection,” Inf. Process. Manag., vol. 58, no. 6, p. 102712, 2021.
Y. Dou, K. Shu, C. Xia, P. S. Yu, and L. Sun, “User preference-aware fake news detection,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 2051–2055.
G. Kim and Y. Ko, “Graph-based fake news detection using a summarization technique,” EACL 2021 - 16th Conf. Eur. Chapter Assoc. Comput. Linguist. Proc. Conf., pp. 3276–3280, 2021, doi: 10.18653/v1/2021.eacl-main.287.
P. Saikia, K. Gundale, A. Jain, D. Jadeja, H. Patel, and M. Roy, “Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach,” in 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, 2022, pp. 1–8.
U. Jeong, K. Ding, L. Cheng, R. Guo, K. Shu, and H. Liu, “Nothing stands alone: Relational fake news detection with hypergraph neural networks,” in 2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022, pp. 596–605.
G. Barnabò et al., “FbMultiLingMisinfo: Challenging large-scale multilingual benchmark for misinformation detection,” in 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, 2022, pp. 1–8.
M. Mayank, S. Sharma, and R. Sharma, “DEAP-FAKED: Knowledge graph based approach for fake news detection,” in 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2022, pp. 47–51.
M. Dhawan, S. Sharma, A. Kadam, R. Sharma, and P. Kumaraguru, “Game-on: Graph attention network based multimodal fusion for fake news detection,” arXiv Prepr. arXiv2202.12478, 2022.
J. Wu and B. Hooi, “DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 2582–2593.
E. Masciari, V. Moscato, A. Picariello, and G. Sperli, “A deep learning approach to fake news detection,” in Foundations of Intelligent Systems: 25th International Symposium, ISMIS 2020, Graz, Austria, September 23–25, 2020, Proceedings, Springer, 2020, pp. 113–122.
K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big data, vol. 8, no. 3, pp. 171–188, 2020.
D. Zhang and V. I. Zadorozhny, “Fake news detection based on subjective opinions,” in Advances in Databases and Information Systems: 24th European Conference, ADBIS 2020, Lyon, France, August 25–27, 2020, Proceedings 24, Springer, 2020, pp. 108–121.
M. H. Al-Tai, B. M. Nema, and A. Al-Sherbaz, “Deep learning for fake news detection: Literature review,” Al-Mustansiriyah J. Sci., vol. 34, no. 2, pp. 70–81, 2023.
T. Chakraborty, “Multi-modal fake news detection,” in Data Science for Fake News: Surveys and Perspectives, Springer, 2020, pp. 41–70.
J. Ma, W. Gao, and K.-F. Wong, “Detect rumors in microblog posts using propagation structure via kernel learning,” Association for Computational Linguistics, 2017.
S. Gong, R. O. Sinnott, J. Qi, and C. Paris, “Fake news detection through graph-based neural networks: A survey,” arXiv Prepr. arXiv2307.12639, 2023.
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