README.md

Information Disorder Awareness (IDA)

IDA develops a comprehensive framework to monitor, detect, and mitigate information disorder online. The project focuses on disinformation, coordinated manipulation, and the cognitive vulnerabilities exploited in social platforms. It combines AI, data mining, OSINT, and explainable machine learning to assess source credibility, verify claims, and identify propaganda or synthetic media. IDA delivers operational tools such as a real-time monitoring platform, a cognitive risk indicator, and multimodal detection services, supporting analysts, public institutions, and media operators in building societal resilience against large-scale manipulation.

Links

• SERICS website: https://serics.eu/services/spoke-2-disinformazione-e-fake-news/
• GitHub: https://github.com/Information-Disorder-Awareness

Publications

• Amendola, M., Cavaliere, D., De Maio, C., Fenza, G., & Loia, V. (2024). Towards Echo Chamber Assessment by employing Aspect-based Sentiment Analysis and GDM Consensus metrics. Online Social Networks and Media.
• Francesco Amoretti, Introduzione. La Geopolitica della disinformazione, on "Comunicazione politica, Quadrimestrale dell'Associazione Italiana di Comunicazione Politica" 2/2023, pp. 157-174, doi: 10.3270/108042. https://doi.org/10.3270/108042.
• Nicola Capuano, Giuseppe Fenza, Vincenzo Loia, Francesco David Nota: Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review. Neurocomputing 530: 91-103 (2023).
• Giuseppe Fenza, Vincenzo Loia, Paola Montserrat Mainardi, Claudio Stanzione: OSINT Knowledge Graph for Fact-Checking: Google Map Hacks Debunking. ITASEC 2023.
• Micaela Bangerter, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Alberto Volpe, Carmen De Maio, and Claudio Stanzione. 2023. Unisa at SemEval-2023 Task 3: A SHAP-based method for Propaganda Detection. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 885–891, Toronto, Canada. Association for Computational Linguistics.
• N. Capuano, G. Fenza, V. Loia and C. Stanzione, "Explainable Artificial Intelligence in CyberSecurity: A Survey", in IEEE Access, vol. 10, pp. 93575-93600, 2022, doi: 10.1109/ACCESS.2022.3204171.
• D. Cavaliere, M. Gallo, C. Stanzione, “Propaganda Detection Robustness through Adversarial Attacks driven by eXplainable AI”, the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28 2023, Lisbon, Portugal.
• Fenza, G., Gallo, M., Loia, V., Petrone, A., & Stanzione, C. (2023). Concept-drift detection index based on fuzzy formal concept analysis for fake news classifiers. Technological Forecasting and Social Change, 194, 122640.
• Becattini, F., Bisogni, C., Loia, V., Pero, C., & Hao, F. (2023). Head Pose Estimation Patterns as Deepfake Detectors. ACM Transactions on Multimedia Computing, Communications and Applications. [https://doi.org/10.1145/3612928.
• Gaeta, A., Loia, V., & Orciuoli, F. (2023). An explainable prediction method based on Fuzzy Rough Sets, TOPSIS and Hexagons of opposition: applications to the analysis of Information Disorder. Information Sciences, 120050.
• Gaeta, A., Orciuoli, F., & Pascuzzo, A. (2023). Satiric Content Detection Through Linguistic Features. In Machine Learning and Artificial Intelligence (pp. 114-119). IOS Press.
• Damiano, E., Gaeta, A., & Orciuoli, F. (2023, August). Selecting a Reduced Set of Features for Supporting the Stance Detection Task. In International Conference on Intelligent Networking and Collaborative Systems (pp. 125-135). Cham: Springer Nature Switzerland.
• Luisa Gargano and Adele A. Rescigno. An FPT Algorithm for Spanning Trees with Few Branch Vertices Parameterized by Modular-Width. In Proceedings of 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023). LIPIcs series; volume: 272; Article No. 50; pp. 50:1–50:15; 2023.
• Fenza G., Loia V., Mainardi P.M., Stanzione C., "OSINT Knowledge Graph for Fact-Checking: Google Map Hacks Debunking", Italian Conference on Cyber Security (ITASEC 2023), CEUR Workshop Proceedings, 3488.
• Cavaliere D., Fenza G., Loia V., Nota F., Emotion-Aware Monitoring of Users’ Reaction With a Multi-Perspective Analysis of Long-and Short-Term Topics on Twitter (2023) International Journal of Interactive Multimedia and Artificial Intelligence, 8 (4), pp. 166 - 175.
• Berjawi O., Fenza G., Loia V., A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions (2023) IEEE Access, 11, pp. 120463 - 120491.
• Cavaliere D., Gallo M., Stanzione C., Propaganda Detection Robustness Through Adversarial Attacks Driven by eXplainable AI, World Conference on Explainable Artificial Intelligence (2023), Communications in Computer and Information Science, 1902 CCIS, pp. 405 - 419.
• Bangerter M.L., Fenza G., Furno D., Gallo M., Loia V., Stanzione C., You I., A Hybrid Framework Integrating LLM and ANFIS for Explainable Fact-Checking, (2024) IEEE Transactions on Fuzzy Systems, pp. 1-11.
• Capuano N., Fenza G., Gallo M., Loia V., Stanzione C., Unfolding the Misinformation Spread: An In-Depth Analysis Through Explainable Link Predictions and Data Mining, (2024) Lecture Notes in Networks and Systems, 1049 LNNS, pp. 137 - 146.
• De Maio C., Fenza G., Furno D., Grauso T., Loia V., A Multi-Agent Architecture for Privacy-Preserving Natural Language Interaction with FHIR-Based Electronic Health Records, (2024) 2024 32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024.
• Cavaliere D., Fenza G., Furno D., Loia V., A semantic model bridging DISARM framework and Situation Awareness for disinformation Attacks Attribution, 2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024, pp. 55 - 62.
• Fenza, G., Gallo, M., Loia, V., Nicolosi, A., & Stanzione, C. (2024, September). Detecting Jailbreaking Prompts: an Anti-Persuasion Filter Framework. In International Conference on Advances in Social Networks Analysis and Mining (pp. 165-179). Cham: Springer Nature Switzerland.
• Fenza G., Gallo M., Loia V., Stanzione C., Evaluating Web Domain Credibility: A Multifactorial Score for Analyzing Online Reliability, (2024) IEEE Conference on Evolving and Adaptive Intelligent Systems.
• Berjawi O., Khatoun R., Fahs W., Fenza G., Leveraging Sentiment and Emotion Analysis to Enhance Cyberbullying Detection, International Conference on Social Networks Analysis, Management and Security, SNAMS, (2024), pp. 81 - 86.
• Fenza G., Froncillo S., Stanzione C., Enhancing Fraud Detection through Cascading Machine Learning Models and Clustering Techniques, Italian Conference on Cyber Security (ITASEC 2024), CEUR Workshop Proceedings, 3731.
• De Maio, C., Fenza, G., Gallo, M. et al. A Perceived Risk Index Leveraging Social Media Data: Assessing Severity of Fire on Microblogging, (2024) Cognitive Computation, 16 (5), pp. 2724 - 2734.
• Berjawi O., Cavaliere D., Fenza G., Loia V., Understanding radicalization pathways: a framework for assessing diversity in YouTube recommendation systems, (2024) Social Network Analysis and Mining, 14 (1), art. no. 233.
• Fenza, G., Furno, D., Gallo, M., Loia, V., & Trotta, P. P. Claim Verification Leveraging In-context Learning and Retrieval Augmented. In Advances in Social Networks Analysis and Mining: Proceedings of the 16th International Conference on Advances in Social Networks Analysis and Mining—ASONAM 2024 Volume 2 (p. 17). Springer Nature.
• Berjawi, O., Cavaliere, D., & Fenza, G. (2024, September). A Multi-aspect Analysis of Echo Chambers on Video-Sharing Social Media. In International Conference on Advances in Social Networks Analysis and Mining (pp. 197-213). Cham: Springer Nature Switzerland.
• Gisi M.D., Fenza G., Gallo M., Loia V., Stanzione C., Cognitive Filter Bubble: Investigating Bias and Neutrality Vulnerabilities of LLMs in Sensitive Contexts, Italian Conference on Cyber Security (ITASEC 2025), CEUR Workshop Proceedings, 3962.
• Fariello S., Fenza G., Forte F., Gallo M., Marotta M., Distinguishing Human From Machine: A Review of Advances and Challenges in AI-Generated Text Detection, (2025) International Journal of Interactive Multimedia and Artificial Intelligence, 9 (3), pp. 6 - 18.
• Fenza G., Furno D., Loia V., Trotta P.P., Multi-LLM Agents Architecture for Claim Verification, Italian Conference on Cyber Security (ITASEC 2025), CEUR Workshop Proceedings, 3962.
• Berjawi O., Cavaliere D., Fenza G., Khatoun R., "Dynamic Analysis of Influencer Impact on Opinion Formation in Social Networks", International Conference on Web Information Systems Engineering (WISE 2024), (2025) Lecture Notes in Computer Science, 15463 LNCS, pp. 394 - 408.
• Berjawi O., Khatoun R., Fenza G., Digital Persuasion: Understanding the Impact of Online Influencers on Public Opinion, International Conference on Persuasive Technology (2025), Lecture Notes in Computer Science, 15711 LNNS, pp. 117 - 127.
• Fenza G., Gaeta A., Loia V., Orciuoli F., Stanzione C., Explaining vulnerabilities of biased news classifiers through rough sets and granular computing, (2025) Information Sciences, 719, art. no. 122439.
• Berjawi, O., Fenza, G., Khatoun, R., & Loia, V. (2025). Mitigating radicalization in recommender systems by rewiring graph with deep reinforcement learning. Online Social Networks and Media, 48, 100325.
• Baclawski, K. P., Cavaliere, D., Fenza, G., Furno, D., & Loia, V. (2025). A Disinformation Attack Risk Awareness Framework: A Case Study on Incidents Collected by DISARM Foundation. Journal of Information Warfare, 24(2).
• Berjawi, O., Cavaliere, D., Fenza, G. and V. Loia, Understanding radicalization pathways: a framework for assessing diversity in YouTube recommendation systems. Social Network Analysis and Mining 14, 233 (2024). https://doi.org/10.1007/s13278-024-01394-8.
• Giuseppe Fenza, Vincenzo Loia, Claudio Stanzione, Maria Di Gisi, Robustness of models addressing Information Disorder: A comprehensive review and benchmarking study, Neurocomputing, Volume 596, 2024, 127951, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2024.127951.
• Umer, M., Alabdulqader, E. A., Alarfaj, A. A., Cascone, L., & Nappi, M. (2024). Cyberbullying Detection Using PCA Extracted GLOVE Features and RoBERTaNet Transformer Learning Model. IEEE Transactions on Computational Social Systems.
• De Maio, C., Fenza, G., Gallo, M. et al. A Perceived Risk Index Leveraging Social Media Data: Assessing Severity of Fire on Microblogging. Cogn Comput 16, 2724–2734 (2024). https://doi.org/10.1007/s12559-024-10266-4.
• Bisogni, C., Loia, V., Nappi, M., & Pero, C. (2024). Acoustic features analysis for explainable machine learning-based audio spoofing detection. Computer Vision and Image Understanding, 249, 104145.