TruthLens: Graph Analytics for Fake News Detection
Keywords:
Fake News Detection, Graph Neural Network, BERT, Multimodal Fusion, Social Network Analysis, GNN, CLIPAbstract
Fake news has evolved from a minor online problem to a potent instrument for influencing public opinion and un-dermining confidence in digital platforms. Conventional detec-tion systems function similarly to linguistic spell checkers, but they frequently overlook the intricate, well-planned nature of disinformation campaigns. In order to close this gap, TruthLens proposes a hybrid, multimodal framework that integrates graph analytics, computer vision, and natural language processing (NLP). Our approach makes use of PyTorch Geometric (GNNs) to monitor the propagation of information in social networks, BERT for comprehensive semantic text analysis, and CLIP for consistency between text and images. TruthLens identifies structural anomalies that indicate bot networks and organized influence operations by examining not only what is said but also how and by whom it spreads. According to preliminary tests, this layered approach performs significantly better than conventional text-only models, providing both greater accuracy and lucid insights into the mechanisms of digital deception.
Downloads
References
K. Shu, S. Wang, and H. Liu, “Beyond news contents: The role of social context for fake news detection,” ACM Transactions on Information Systems, vol. 38, no. 3, pp. 1–20, May 2020.
A. Kula, S. Ghosh, and S. Ghosh, “Fake news detection using deep learning and natural language processing,” in Proc. IEEE Int. Conf. Big Data, Los Angeles, CA, USA, Dec. 2019, pp. 2876–2884.
N. Ahmed, J. M. A. Rahman, and M. M. Hossain, “Detecting fake news using machine learning: A systematic literature review,” IEEE Access, vol. 8, pp. 203094–203117, 2020.
A. K. Jain and B. Gupta, “A machine learning based approach for detection of fake news using NLP,” in Proc. IEEE Int. Conf. Advances in Computing, Communication Control and Networking, Greater Noida, India, Oct. 2018, pp. 1103–1107.
A. Bondielli and F. Marcelloni, “A survey on fake news and rumour detection techniques,” Information Sciences, vol. 497, pp. 38–55, Sep. 2019.
K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “FakeNewsNet: A data repository with news content, social context, and spatiotemporal information,” Big Data, vol. 8, no. 3, pp. 171–188, Sep. 2020.
S. K. Bharti and K. S. Babu, “Fake news detection using deep learning models: A novel approach,” in Proc. IEEE Int. Conf. Advances in Computing, Communication and Control, Mumbai, India, Dec. 2019,pp. 1–6.
R. Oshikawa, J. Qian, and W. Y. Wang, “A survey on natural language processing for fake news detection,” in Proc. 12th Language Resources and Evaluation Conf., Marseille, France, May 2020, pp. 6086–6093.
S. K. Bharti and K. S. Babu, “Fake news detection using machine learning approaches: A systematic review,” in Proc. IEEE Int. Conf. Electronics, Computing and Communication Technologies, Bangalore, India, Jul. 2020, pp. 1–6.
Y. Zhou and R. Zafarani, “A survey of fake news: Fundamental theories, detection methods, and opportunities,” ACM Computing Surveys, vol. 53, no. 5, pp. 1–40, Sep. 2020.
J. Stro¨mba¨ck et al., “News media trust and its impact on media use,” Annals of the International Communication Association, vol. 44, pp. 139–156, 2020.
E. Mitchelstein and P. J. Boczkowski, “Online news consumption research,” New Media & Society, vol. 12, pp. 1085–1102, 2010.
P. Henrique, A. Faustini, and T. F. Covo˜es, “Fake news detection in multiple platforms and languages,” Expert Systems with Applications, vol. 158, pp. 1–9, 2020.
F. A. Ozbay and B. Alatas, “Fake news detection within online social media using supervised artificial intelligence algorithms,” Physica A, vol. 540, pp. 1–19, 2020.
C. Castillo, M. Mendoza, and B. Poblete, “Information credibility on Twitter,” in Proc. 20th Int. Conf. World Wide Web, Hyderabad, India, 2011.
Annadhurai, Hemlathadhevi, Lakshmikanth Paleti, Ramesh Kumar Chinnakunnu, and Surendran Rajendran. "A data-driven approach to vertical harvesting: artificial intelligence and internet of things-based automation in agriculture." Agrociencia (2026): 1-19.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” arXiv:1607.00653, 2016.D. Pomerleau and D. Rao, “Fake news challenge,” 2017. [Online]. Available: http://www.fakenewschallenge.org
Mariappan, Dineshbabu, Vijay Ramalingam, and Ramesh Kumar. "Energy Reliance (ER) Based Co-Operative Gaming (COG) Model for Connectivity Establishment in WSNS." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 393-398. IEEE, 2020.
Hemlathadhevi, A., D. THIRUPURASUNDARI, C. RAMESH KUMAR, and G. Nirmalar. "An anonymous mutual authentication mechanism for wearable sensors in three-tier mobile healthcare systems." Journal of Theoretical and Applied Information Technology 102, no. 5 (2024).
Chakole, Nitin, Krishna Priya Remamany, G. Mohan, P. Sasirekha, N. M. G. Kumar, C. Ramesh Kumar, M. Shunmugasundaram, Manjunathan Alagarsamy, and Bibhu Prasad Ganthia. "Optimal energy management for hybrid PV-wind-battery microgrids through markov decision processes technique." International Journal of Smart Grid-ijSmartGrid 9, no. 3 (2025): 127-145.
E. Buabin, “Boosted hybrid recurrent neural classifier for text document classification,” Int. J. Machine Learning and Computing, vol. 2, pp. 588–592, 2012.
I. Dilrukshi and K. De Zoysa, “A feature selection method for Twitter news classification,” Int. J. Machine Learning and Computing, vol. 4, pp. 365–370, 2014.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kushagra Kumar Porwal, Ayushi Singh
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
