{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T03:08:37Z","timestamp":1779160117128,"version":"3.51.4"},"reference-count":125,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.knosys.2026.115305","type":"journal-article","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T22:46:33Z","timestamp":1768171593000},"page":"115305","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["An Evaluation of XAI Methods applied to Information Disorder Detection Models"],"prefix":"10.1016","volume":"336","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1427-4748","authenticated-orcid":false,"given":"Mark Nicolas","family":"Gruensteidl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-7718","authenticated-orcid":false,"given":"Sabrina","family":"Kirrane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115305_bib0001","series-title":"Council of Europe report","article-title":"Information disorder: Toward an interdisciplinary framework for research and policy making","author":"Wardle","year":"2017"},{"key":"10.1016\/j.knosys.2026.115305_bib0002","unstructured":"E. Ortiz-Ospina, The rise of social media, 2019. https:\/\/ourworldindata.org\/rise-of-social-media."},{"key":"10.1016\/j.knosys.2026.115305_bib0003","doi-asserted-by":"crossref","DOI":"10.1007\/s13278-023-01028-5","article-title":"Fake news, disinformation and misinformation in social media: a review","volume":"13","author":"Aimeur","year":"2023","journal-title":"Social Network Analysis and Mining"},{"key":"10.1016\/j.knosys.2026.115305_bib0004","series-title":"Digital Democracy, Social Media and Disinformation","author":"Iosifidis","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0005","article-title":"Can artificial intelligence help end fake news?","author":"Cassauwers","year":"2019","journal-title":"Horiz. Magaz."},{"key":"10.1016\/j.knosys.2026.115305_bib0006","unstructured":"U. Nations, UN tackles \u2018infodemic\u2019 of misinformation and cybercrime in COVID-19 crisis, 2020. https:\/\/www.un.org\/en\/un-coronavirus-communications-team\/un-tackling-%E2%80%98infodemic%E2%80%99-misinformation-and-cybercrime-covid-19."},{"key":"10.1016\/j.knosys.2026.115305_bib0007","article-title":"EU medicines agency says hackers manipulated leaked coronavirus vaccine data","author":"Cerulus","year":"2021","journal-title":"Politico"},{"key":"10.1016\/j.knosys.2026.115305_bib0008","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-pu-42-012821-100001","article-title":"More on Fake News, Disinformation, and Countering These with Science","volume":"42","author":"Green","year":"2021","journal-title":"Annu. Rev. Public Health"},{"key":"10.1016\/j.knosys.2026.115305_bib0009","series-title":"Insight Report","article-title":"The Global Risks Report 2025","author":"Elsner","year":"2025"},{"key":"10.1016\/j.knosys.2026.115305_bib0010","unstructured":"R.D. Caballar, 10\u202fAI dangers and risks and how to manage them, 2025. https:\/\/www.ibm.com\/think\/insights\/10-ai-dangers-and-risks-and-how-to-manage-them."},{"key":"10.1016\/j.knosys.2026.115305_bib0011","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.479","article-title":"To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods","volume":"7","author":"Amparore","year":"2021","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.knosys.2026.115305_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.jrt.2025.100128","article-title":"A turning point in AI: Europe\u2019s human-centric approach to technology regulation","volume":"23","author":"Balcio\u01e7lu","year":"2025","journal-title":"J. Respons. Technol."},{"key":"10.1016\/j.knosys.2026.115305_bib0013","first-page":"9","article-title":"Web 2.0: New Challenges for the Study of E-Democracy in an Era of Informational Exuberance","volume":"5","author":"Chadwick","year":"2009","journal-title":"I\/S: A J. Law Policy Inform. Soc."},{"key":"10.1016\/j.knosys.2026.115305_bib0014","first-page":"145","article-title":"Theoretical models of voting behaviour","volume":"4","author":"Antunes","year":"2010","journal-title":"Exedra"},{"key":"10.1016\/j.knosys.2026.115305_bib0015","series-title":"The impact of Disinformation on Democratic Processes and Human Rights in the World","author":"COLOMINA","year":"2021"},{"key":"10.1016\/j.knosys.2026.115305_bib0016","first-page":"41","article-title":"Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency","author":"Vese","year":"2021","journal-title":"Eur. J. Risk Regulat."},{"key":"10.1016\/j.knosys.2026.115305_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102140","article-title":"Fake news detection: Taxonomy and comparative study","volume":"103","author":"Farhangian","year":"2024","journal-title":"Inform. Fusion"},{"key":"10.1016\/j.knosys.2026.115305_bib0018","doi-asserted-by":"crossref","DOI":"10.1007\/s13735-023-00296-3","article-title":"A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunities","volume":"12","author":"Tufchi","year":"2023","journal-title":"International Journal of Multimedia Information Retrieval"},{"issue":"4","key":"10.1016\/j.knosys.2026.115305_bib0019","first-page":"745","article-title":"Semantic Web technologies and bias in artificial intelligence: A systematic literature review","volume":"14","author":"Reyero Lobo","year":"2023","journal-title":"Semantic Web"},{"key":"10.1016\/j.knosys.2026.115305_bib0020","series-title":"2022 IEEE International Conference on Big Data (Big Data)","first-page":"5731","article-title":"Exploring the Generalisability of Fake News Detection Models","author":"Hoy","year":"2022"},{"key":"10.1016\/j.knosys.2026.115305_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102295","article-title":"HViT: Hybrid vision inspired transformer for the assessment of carotid artery plaque by addressing the cross-modality domain adaptation problem in MRI","volume":"109","author":"Ayoub","year":"2023","journal-title":"Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society"},{"key":"10.1016\/j.knosys.2026.115305_bib0022","doi-asserted-by":"crossref","first-page":"458","DOI":"10.3390\/systems11090458","article-title":"Sustainable Development of Information Dissemination: A Review of Current Fake News Detection Research and Practice","volume":"11","author":"Yuan","year":"2023","journal-title":"Systems"},{"key":"10.1016\/j.knosys.2026.115305_bib0023","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"\u201dWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier","author":"Ribeiro","year":"2016"},{"key":"10.1016\/j.knosys.2026.115305_bib0024","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-03100-6","article-title":"New explainability method for BERT-based model in fake news detection","volume":"11","author":"Szczepa\u0144ski","year":"2021","journal-title":"Scientific Reports"},{"key":"10.1016\/j.knosys.2026.115305_bib0025","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.knosys.2026.115305_bib0026","article-title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","volume":"abs\/1312.6034","author":"Simonyan","year":"2013","journal-title":"CoRR"},{"key":"10.1016\/j.knosys.2026.115305_bib0027","unstructured":"Z. Wu, D.C. Ong, On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification, arXiv: 2101.00196. (2021). https:\/\/api.semanticscholar.org\/CorpusID:230437868."},{"key":"10.1016\/j.knosys.2026.115305_bib0028","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1162\/tacl_a_00511","article-title":"Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond","volume":"10","author":"Feder","year":"2022","journal-title":"Transact. Assoc. Comput. Linguist."},{"key":"10.1016\/j.knosys.2026.115305_bib0029","series-title":"Integrated Science in Digital Age 2020","first-page":"13","article-title":"Approaches to Identify Fake News: A Systematic Literature Review","author":"de Beer","year":"2021"},{"issue":"2","key":"10.1016\/j.knosys.2026.115305_bib0030","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s11227-020-03294-y","article-title":"DeepFakE: improving fake news detection using tensor decomposition-based deep neural network","volume":"77","author":"Kaliyar","year":"2021","journal-title":"The Journal of Supercomputing"},{"key":"10.1016\/j.knosys.2026.115305_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131711","article-title":"A fake news detection framework integrating multi-domain and multimodal features","volume":"658","author":"Guo","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.115305_bib0032","article-title":"A Comparison and Critical Reflection of Information Disorder Detection Techniques: Performing a Cross-Data and Cross-Model Evaluation","author":"Gruensteidl","year":"2025","journal-title":"Inform. Fusion"},{"issue":"16","key":"10.1016\/j.knosys.2026.115305_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e35865","article-title":"GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection","volume":"10","author":"Dhiman","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.knosys.2026.115305_bib0034","unstructured":"S. Gonz\u2019alez-Silot, A. Montoro-Montarroso, E. Mart\u00ednez-C\u00e1mara, J. G\u2019omez-Romero, Enhancing Disinformation Detection with Explainable AI and Named Entity Replacement, arXiv: 2502.04863. (2025). https:\/\/api.semanticscholar.org\/CorpusID:276236052."},{"key":"10.1016\/j.knosys.2026.115305_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124710","article-title":"Survey on Explainable AI: Techniques, challenges and open issues","volume":"255","author":"Abusitta","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.knosys.2026.115305_bib0036","unstructured":"D. Gunning, Explainable Artificial Intelligence (XAI): Program Update, 2017. https:\/\/nsarchive.gwu.edu\/document\/18382-national-security-archive-david-gunning-darpa."},{"key":"10.1016\/j.knosys.2026.115305_bib0037","series-title":"Proceedings of Sixth International Conference on Computer and Communication Technologies","first-page":"317","article-title":"Detection of Fake News Using Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Algorithms","author":"Srivastava","year":"2025"},{"key":"10.1016\/j.knosys.2026.115305_bib0038","first-page":"1081","article-title":"Integrating Explainable AI with Enhanced Ensemble Models for Accurate and Transparent Fake News Detection in OSN\u2019s","volume":"258","author":"Kondamudi","year":"2025","journal-title":"Int. Conferen. Mach. Learn. Data Eng."},{"issue":"7","key":"10.1016\/j.knosys.2026.115305_bib0039","doi-asserted-by":"crossref","DOI":"10.1145\/3711123","article-title":"Decoding Fake News and Hate Speech: A Survey of Explainable AI Techniques","volume":"57","author":"Ngueajio","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2026.115305_bib0040","unstructured":"K. Devireddy, A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches, arXiv: 2504.04276. (2025). https:\/\/api.semanticscholar.org\/CorpusID:277621779."},{"issue":"23","key":"10.1016\/j.knosys.2026.115305_bib0041","doi-asserted-by":"crossref","DOI":"10.3390\/s21238020","article-title":"Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses","volume":"21","author":"Nor","year":"2021","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.knosys.2026.115305_bib0042","doi-asserted-by":"crossref","DOI":"10.3390\/e23010018","article-title":"Explainable AI: A Review of Machine Learning Interpretability Methods","volume":"23","author":"Linardatos","year":"2021","journal-title":"Entropy"},{"key":"10.1016\/j.knosys.2026.115305_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128450","article-title":"Towards explainable fake news detection and automated content credibility assessment: Polish internet and digital media use-case","volume":"608","author":"Kozik","year":"2024","journal-title":"Neurocomputing"},{"issue":"22","key":"10.1016\/j.knosys.2026.115305_bib0044","doi-asserted-by":"crossref","DOI":"10.3390\/electronics10222739","article-title":"Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification","volume":"10","author":"Lovera","year":"2021","journal-title":"Electronics"},{"key":"10.1016\/j.knosys.2026.115305_bib0045","unstructured":"Y. Hechtlinger, Interpretation of Prediction Models Using the Input Gradient, arXiv: 1611.07634. (2016). https:\/\/api.semanticscholar.org\/CorpusID:10814745."},{"key":"10.1016\/j.knosys.2026.115305_bib0046","doi-asserted-by":"crossref","DOI":"10.1609\/aaaiss.v4i1.31765","article-title":"Limitations of Feature Attribution in Long Text Classification of Standards","author":"Beckh","year":"2024","journal-title":"Proceed. AAAI Sympos. Ser."},{"key":"10.1016\/j.knosys.2026.115305_bib0047","series-title":"Text, Speech, and Dialogue","first-page":"263","article-title":"BERT-based Classifiers for Fake News Detection on Short and Long Texts with Noisy Data: A Comparative Analysis","author":"Shushkevich","year":"2022"},{"key":"10.1016\/j.knosys.2026.115305_bib0048","unstructured":"M. Seb\u0151k, V. Kov\u00e1cs, M. B\u2019an\u2019oczy, D.M. Eriksen, N. Neptune, P. Roussille, Beyond Token Limits: Assessing Language Model Performance on Long Text Classification, arXiv: 2509.10199. (2025). https:\/\/api.semanticscholar.org\/CorpusID:281310358."},{"key":"10.1016\/j.knosys.2026.115305_bib0049","first-page":"245","article-title":"A Survey on the Explainability of Supervised Machine Learning","volume":"70","author":"Burkart","year":"2021","journal-title":"J. Artif. Int. Res."},{"issue":"C","key":"10.1016\/j.knosys.2026.115305_bib0050","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ins.2022.10.013","article-title":"Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey","volume":"615","author":"Ding","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.knosys.2026.115305_bib0051","article-title":"A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks","author":"Islam","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2026.115305_bib0052","unstructured":"K. Fauvel, V. Masson, E. Fromont, A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers, arXiv: 2005.14501. (2020). https:\/\/api.semanticscholar.org\/CorpusID:219124194."},{"issue":"1","key":"10.1016\/j.knosys.2026.115305_bib0053","article-title":"Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond","volume":"24","author":"Hedstr\u00f6m","year":"2023","journal-title":"J. Mach. Learn. Res."},{"issue":"19","key":"10.1016\/j.knosys.2026.115305_bib0054","doi-asserted-by":"crossref","DOI":"10.3390\/app12199423","article-title":"XAI Systems Evaluation: A Review of Human and Computer-Centred Methods","volume":"12","author":"Lopes","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2026.115305_bib0055","series-title":"Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","article-title":"What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks","author":"Sokol","year":"2024"},{"key":"10.1016\/j.knosys.2026.115305_bib0056","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.knosys.2026.115305_bib0057","doi-asserted-by":"crossref","DOI":"10.3390\/electronics10050593","article-title":"Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics","volume":"10","author":"Zhou","year":"2021","journal-title":"Electronics"},{"issue":"13s","key":"10.1016\/j.knosys.2026.115305_bib0058","doi-asserted-by":"crossref","DOI":"10.1145\/3583558","article-title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI","volume":"55","author":"Nauta","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2026.115305_bib0059","doi-asserted-by":"crossref","unstructured":"N. Kotonya, F. Toni, Towards a Framework for Evaluating Explanations in Automated Fact Verification, arXiv: 2403.20322. (2024). https:\/\/api.semanticscholar.org\/CorpusID:268793387.","DOI":"10.63317\/3kcoqz4uix8g"},{"key":"10.1016\/j.knosys.2026.115305_bib0060","unstructured":"L. Schoenegger, Y. Xia, B. Roth, An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text, arXiv: 2408.14252. (2024). https:\/\/api.semanticscholar.org\/CorpusID:271957532."},{"key":"10.1016\/j.knosys.2026.115305_bib0061","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"286","article-title":"Adversarial Infidelity Learning for Model Interpretation","author":"Liang","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0062","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"9525","article-title":"Sanity checks for saliency maps","author":"Adebayo","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0063","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"885","article-title":"Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis","author":"Cheng","year":"2019"},{"key":"10.1016\/j.knosys.2026.115305_bib0064","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","first-page":"2931","article-title":"Is Attention Interpretable?","author":"Serrano","year":"2019"},{"key":"10.1016\/j.knosys.2026.115305_bib0065","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"5578","article-title":"Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection","author":"Chen","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0066","unstructured":"J. Chen, L. Song, M.J. Wainwright, M.I. Jordan, L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data, arXiv: 1808.02610. (2018). https:\/\/api.semanticscholar.org\/CorpusID:51942590."},{"key":"10.1016\/j.knosys.2026.115305_bib0067","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"5553","article-title":"Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions","author":"Han","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0068","series-title":"Annual Meeting of the Association for Computational Linguistics","article-title":"Towards Transparent and Explainable Attention Models","author":"Mohankumar","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0069","series-title":"Advances in Neural Information Processing Systems","first-page":"5968","article-title":"Model Agnostic Multilevel Explanations","volume":"33","author":"Natesan Ramamurthy","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0070","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"883","article-title":"Learning to Explain: An Information-Theoretic Perspective on Model Interpretation","volume":"80","author":"Chen","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0071","series-title":"Annual Meeting of the Association for Computational Linguistics","article-title":"NILE : Natural Language Inference with Faithful Natural Language Explanations","author":"Kumar","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0072","unstructured":"J. Li, L. Liu, H. Li, G. Li, G. Huang, S. Shi, Evaluating Explanation Methods for Neural Machine Translation, arXiv: 2005.01672. (2020). https:\/\/api.semanticscholar.org\/CorpusID:218487295."},{"key":"10.1016\/j.knosys.2026.115305_bib0073","series-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","first-page":"4244","article-title":"Beyond polarity: interpretable financial sentiment analysis with hierarchical query-driven attention","author":"Luo","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0074","series-title":"Advances in Neural Information Processing Systems","first-page":"20554","article-title":"On Completeness-aware Concept-Based Explanations in Deep Neural Networks","volume":"33","author":"Yeh","year":"2020"},{"issue":"01","key":"10.1016\/j.knosys.2026.115305_bib0075","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1609\/aaai.v33i01.33015717","article-title":"Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods","volume":"33","author":"Yuan","year":"2019","journal-title":"Proceed. AAAI Conferen. Artificial Intell."},{"key":"10.1016\/j.knosys.2026.115305_bib0076","series-title":"Neural Information Processing Systems","article-title":"Model Agnostic Supervised Local Explanations","author":"Plumb","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0077","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"5628","article-title":"Robust and Stable Black Box Explanations","volume":"119","author":"Lakkaraju","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0078","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"4157","article-title":"Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations","author":"Camburu","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0079","series-title":"Advances in Neural Information Processing Systems","article-title":"Towards Robust Interpretability with Self-Explaining Neural Networks","volume":"31","author":"Alvarez Melis","year":"2018"},{"issue":"6","key":"10.1016\/j.knosys.2026.115305_bib0080","first-page":"373","article-title":"Survey and critique of techniques for extracting rules from trained artificial neural networks","volume":"8","author":"Andrews","year":"1995","journal-title":"Knowl. Base. Neur. Netw."},{"key":"10.1016\/j.knosys.2026.115305_bib0081","unstructured":"M. Honegger, Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions, arXiv: 1808.05054. (2018). https:\/\/api.semanticscholar.org\/CorpusID:52012603."},{"key":"10.1016\/j.knosys.2026.115305_bib0082","series-title":"Advances in Neural Information Processing Systems","first-page":"1913","article-title":"Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics","volume":"33","author":"Tseng","year":"2020"},{"issue":"2","key":"10.1016\/j.knosys.2026.115305_sbref0083","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE","volume":"11","author":"Jaccard","year":"1912","journal-title":"New Phytologist"},{"key":"10.1016\/j.knosys.2026.115305_bib0084","series-title":"An Elementary Mathematical Theory of Classification and Prediction","author":"Tanimoto","year":"1958"},{"key":"10.1016\/j.knosys.2026.115305_bib0085","article-title":"A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations","volume":"abs\/1805.07039","author":"Nie","year":"2018","journal-title":"CoRR"},{"key":"10.1016\/j.knosys.2026.115305_bib0086","doi-asserted-by":"crossref","unstructured":"P.E. Pope, S. Kolouri, M. Rostami, C.E. Martin, H. Hoffmann, Explainability Methods for Graph Convolutional Neural Networks, 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019) 10764\u201310773. https:\/\/api.semanticscholar.org\/CorpusID:198904065.","DOI":"10.1109\/CVPR.2019.01103"},{"key":"10.1016\/j.knosys.2026.115305_bib0087","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"9046","article-title":"When Explanations Lie: Why Many Modified BP Attributions Fail","volume":"119","author":"Sixt","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0088","series-title":"Advances in Neural Information Processing Systems","article-title":"Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections","volume":"31","author":"Zhang","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0089","series-title":"Fooling Network Interpretation in Image Classification","first-page":"2020","author":"Subramanya","year":"2019"},{"issue":"1","key":"10.1016\/j.knosys.2026.115305_bib0090","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On Information and Sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Statist."},{"key":"10.1016\/j.knosys.2026.115305_bib0091","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113042","article-title":"Evaluating the effectiveness of XAI techniques for encoder-based language models","volume":"310","author":"Mersha","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.knosys.2026.115305_bib0092","series-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18","first-page":"331","article-title":"Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value","author":"Labreuche","year":"2018"},{"key":"10.1016\/j.knosys.2026.115305_bib0093","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"3145","article-title":"Learning Important Features Through Propagating Activation Differences","volume":"70","author":"Shrikumar","year":"2017"},{"issue":"11","key":"10.1016\/j.knosys.2026.115305_bib0094","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1109\/TNNLS.2016.2599820","article-title":"Evaluating the Visualization of What a Deep Neural Network Has Learned","volume":"28","author":"Samek","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.knosys.2026.115305_bib0095","article-title":"An Adversarial Benchmark for Fake News Detection Models","volume":"abs\/2201.00912","author":"Flores","year":"2022","journal-title":"CoRR"},{"key":"10.1016\/j.knosys.2026.115305_bib0096","series-title":"Advances in Neural Information Processing Systems","article-title":"On the (In)fidelity and Sensitivity of Explanations","volume":"32","author":"Yeh","year":"2019"},{"key":"10.1016\/j.knosys.2026.115305_bib0097","unstructured":"N. Puri, S. Verma, P.K. Gupta, D. Kayastha, S. Deshmukh, B. Krishnamurthy, S. Singh, Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution, Computer Vision and Pattern Recognition(2019). https:\/\/api.semanticscholar.org\/CorpusID:214701125."},{"key":"10.1016\/j.knosys.2026.115305_bib0098","unstructured":"C. Singh, W.J. Murdoch, B. Yu, Hierarchical interpretations for neural network predictions, arXiv: 1806.05337. (2018). https:\/\/api.semanticscholar.org\/CorpusID:49214673."},{"key":"10.1016\/j.knosys.2026.115305_bib0099","doi-asserted-by":"crossref","unstructured":"L. Li, R. Ma, Q. Guo, X. Xue, X. Qiu, BERT-ATTACK: Adversarial Attack against BERT Using BERT, arXiv: 2004.09984. (2020). https:\/\/api.semanticscholar.org\/CorpusID:216036179.","DOI":"10.18653\/v1\/2020.emnlp-main.500"},{"key":"10.1016\/j.knosys.2026.115305_bib0100","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"6174","article-title":"BAE: BERT-Based Adversarial Examples for Text Classification","author":"Garg","year":"2020"},{"key":"10.1016\/j.knosys.2026.115305_bib0101","series-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"5053","article-title":"Contextualized Perturbation for Textual Adversarial Attack","author":"Li","year":"2021"},{"key":"10.1016\/j.knosys.2026.115305_bib0102","doi-asserted-by":"crossref","first-page":"11974","DOI":"10.1109\/ACCESS.2021.3051315","article-title":"A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence","volume":"9","author":"Stepin","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.115305_bib0103","article-title":"Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff","volume":"abs\/2107.05693","author":"Naylor","year":"2021","journal-title":"CoRR"},{"key":"10.1016\/j.knosys.2026.115305_bib0104","article-title":"Detecting opinion spams and fake news using text classification","volume":"1","author":"Ahmed","year":"2017","journal-title":"Secur. Priv."},{"key":"10.1016\/j.knosys.2026.115305_bib0105","series-title":"Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques","author":"Ahmed","year":"2017"},{"key":"10.1016\/j.knosys.2026.115305_bib0106","unstructured":"Politifact, Politifact.com, 2025. https:\/\/www.politifact.com\/."},{"key":"10.1016\/j.knosys.2026.115305_bib0107","unstructured":"A. Trikoz, euvsdisinfo dataset winterForestStump\/Roberta-fake-news-detector, 2023, https:\/\/huggingface.co\/datasets\/winterForestStump\/fake-news-detector-euvsdisinfo."},{"key":"10.1016\/j.knosys.2026.115305_bib0108","unstructured":"East Stratcom Task Force - European External Action Service (EEAS), EUvsDisinfo, 2023, https:\/\/euvsdisinfo.eu\/."},{"key":"10.1016\/j.knosys.2026.115305_bib0109","unstructured":"E. Newsroom, European Newsroom, 2024. https:\/\/europeannewsroom.com\/."},{"key":"10.1016\/j.knosys.2026.115305_bib0110","unstructured":"V. Morini, L. Bellomo, R. Giulio, D. Pedreschi, P. Ferragina, European Multilingual News Articles Dataset with Topic Annotation, 2023, 10.5281\/zenodo.10397399."},{"key":"10.1016\/j.knosys.2026.115305_bib0111","unstructured":"C. Crawl, Common Crawl, 2016. https:\/\/commoncrawl.org\/blog\/news-dataset-available."},{"key":"10.1016\/j.knosys.2026.115305_bib0112","unstructured":"J. Fu, jy46604790\/Fake-News-Bert-Detect, 2022. https:\/\/huggingface.co\/jy46604790\/Fake-News-Bert-Detect."},{"key":"10.1016\/j.knosys.2026.115305_bib0113","series-title":"Proceedings of the 34th International Conference on Machine Learning - Volume 70","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017"},{"key":"10.1016\/j.knosys.2026.115305_bib0114","unstructured":"V. Dibia, How to Implement Gradient Explanations for a HuggingFace Text Classification Model (Tensorflow 2.0), 2022. https:\/\/victordibia.com\/blog\/explain-bert-classification\/."},{"issue":"11","key":"10.1016\/j.knosys.2026.115305_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.apr.2025.102672","article-title":"Development and interpretation of PM2.5 estimation model for the Seoul Metropolitan Area using machine learning and explainable AI","volume":"16","author":"Kim","year":"2025","journal-title":"Atmospheric Pollution Research"},{"key":"10.1016\/j.knosys.2026.115305_bib0116","unstructured":"Y. Wang, T. Zhang, X. Guo, Z. Shen, Gradient based Feature Attribution in Explainable AI: A Technical Review, arXiv: 2403.10415. (2024). https:\/\/api.semanticscholar.org\/CorpusID:268510511."},{"key":"10.1016\/j.knosys.2026.115305_bib0117","series-title":"Advances in Neural Information Processing Systems","first-page":"26478","article-title":"Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network","volume":"35","author":"Jeon","year":"2022"},{"issue":"2","key":"10.1016\/j.knosys.2026.115305_bib0118","doi-asserted-by":"crossref","DOI":"10.1145\/3639372","article-title":"Explainability for Large Language Models: A Survey","volume":"15","author":"Zhao","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.knosys.2026.115305_bib0119","unstructured":"A. Taly, Explaining Machine Learning Models, 2020. Ankur Taly, Fiddler Labs Alternative link: https:\/\/theory.stanford.edu\/~ataly\/Talks\/ExplainingMLModels.pdf, https:\/\/theory.stanford.edu\/~ataly\/Talks\/ExplainingMLModels-Mar2020.pdf."},{"key":"10.1016\/j.knosys.2026.115305_bib0120","unstructured":"E. Lindwurm, Basics: Gradient*Input as Explanation, 2021, Originally published on: Gradient*Input as Explanation https:\/\/towardsdatascience.com\/basics-gradient-input-as-explanation-bca79bb80de0, https:\/\/medium.com\/data-science\/basics-gradient-input-as-explanation-bca79bb80de0."},{"key":"10.1016\/j.knosys.2026.115305_bib0121","series-title":"Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Textual Classification Models via Adversarial Perturbation","author":"Burger","year":"2023"},{"key":"10.1016\/j.knosys.2026.115305_bib0122","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111107","article-title":"A unified and practical user-centric framework for explainable artificial intelligence","volume":"283","author":"Kaplan","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.knosys.2026.115305_bib0123","unstructured":"L. Lunga, S. Sreehari, Undermining Image and Text Classification Algorithms Using Adversarial Attacks, arXiv: 2411.03348. (2024). https:\/\/api.semanticscholar.org\/CorpusID:273849923."},{"key":"10.1016\/j.knosys.2026.115305_bib0124","doi-asserted-by":"crossref","unstructured":"X. Huang, J. Marques-Silva, On the failings of Shapley values for explainability, Synergies between Machine Learning and Reasoning 171 (2024) 109112. 10.1016\/j.ijar.2023.109112.","DOI":"10.1016\/j.ijar.2023.109112"},{"key":"10.1016\/j.knosys.2026.115305_bib0125","doi-asserted-by":"crossref","first-page":"137472","DOI":"10.1109\/ACCESS.2024.3463948","article-title":"Problems With SHAP and LIME in Interpretable AI for Education: A Comparative Study of Post-Hoc Explanations and Neural-symbolic Rule Extraction","volume":"12","author":"Hooshyar","year":"2024","journal-title":"IEEE Access"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126000493?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126000493?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T02:48:30Z","timestamp":1779158910000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126000493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":125,"alternative-id":["S0950705126000493"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115305","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An Evaluation of XAI Methods applied to Information Disorder Detection Models","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115305","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"115305"}}