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Advances in Information Quality: Fact-Checking and AI in the Era...
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Advances in Information Quality: Fact-Checking and AI in the Era of Fake News

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 11750

Editor

Dr. Włodzimierz Lewoniewski

E-Mail Website
Guest Editor
Department of Information Systems, Poznan University of Economics, 61-875 Poznań, Poland
Interests: information quality; open data; NLP; semantic web; fake news
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to address the challenges posed by digital misinformation by leveraging advancements in Artificial Intelligence and Large Language Models. High-quality submissions are sought that explore various aspects of information quality, fact-checking, and the role of AI in combating fake news. Contributions from academic researchers, industry professionals, and fact-checking agencies are particularly welcome. 

Fact-checking agencies are encouraged to submit their valuable expertise and insights to this Special Issue. Practical experiences and case studies in the realm of fact-checking are invaluable to the collective understanding and advancement of information quality.

Topics of interest include, but are not limited to:

  • Information Quality and Source Reliability:
    • Principles and metrics for assessing information quality.
    • Identifying reliable sources of information.
    • Comparison of information sources arcoss topics and languages.
    • Improving the quality of information by combining different sources.
  • Artificial Intelligence and Large Language Models:
    • Automation of fact-checking processes using AI.
    • Innovations in Large Language Models enhancing accuracy and efficiency.
    • Identifying and prioritizing check-worthy information.
  • Combatting Fake News:
    • Strategies and technological solutions for detecting and mitigating misinformation.
    • Case studies demonstrating successful implementations.
    • Ethical considerations and the philosophy of information in the context of fake news.
  • Information Intelligence and Extraction:
    • Data mining techniques for information verification.
    • Knowledge management practices for handling vast datasets.
    • AI methods for information extraction and fact discernment.
    • Named entity recognition and topic modeling.
  • Applications and Impacts:
    • AI’s role in enhancing the human–machine interface for verification.
    • Social implications of improved information quality.
    • Business process management through reliable information dissemination.

Manuscripts should be submitted online at the Information Journal website. There is no restriction on the maximum length of manuscripts, and full experimental details must be provided to ensure results can be reproduced. 

We look forward to your valuable contributions to this Special Issue on enhancing Information Quality through AI-driven Fact-checking and combating fake news.

Dr. Włodzimierz Lewoniewski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information quality
  • fact checking
  • fake news
  • artificial intelligence
  • large language models
  • check worthiness

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

36 pages, 626 KB  
Article
Comparative Performance of AI-Generated Fake News Detection Pipelines on Romanian News Content
by Claudiu Coman, Costel Marian Dalban, Vlad Bătrânu-Pințea, Georgiana Aron and Lucian Marina
Information 2026, 17(7), 698; https://doi.org/10.3390/info17070698 - 18 Jul 2026
Viewed by 813
Abstract
Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. [...] Read more.
Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. The study is based on source code generated with the assistance of artificial intelligence systems for a set of machine learning and transformer-based models. The code was subsequently implemented in Google Colab. 2026, trained on international benchmark datasets, and tested on Romanian news content. This design allowed the rapid prototyping of multiple detection pipelines and the systematic observation of their behavior in a media environment different from that represented in the training corpora. The models were evaluated comparatively using standard classification metrics, including accuracy, precision, recall, and F1-score, complemented by additional indicators relevant to model robustness and practical usability. The experimental results revealed significant differences in performance across algorithms when applied to English translations of Romanian-language news content after training on international datasets. However, this study does not provide a direct comparison between model performance on the international benchmark datasets and the Romanian test corpus; therefore, the gap between the international training corpus and the Romanian-sourced test corpus is interpreted as an exploratory limitation and as a direction for future research. Based on these findings, we propose an empirical classification of the tested models according to their predictive effectiveness, their contextual robustness across linguistic environments, and their operational relevance as filtering tools for institutional monitoring. The results show that AI-assisted coding workflows can provide a viable starting point for reproducible misinformation research, but they also underline the limitations of directly transferring models trained on non-Romanian data to local media ecosystems. The study offers both a replicable evaluation framework and practical insights for institutions involved in strategic communication, public security, and the monitoring of information threats. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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23 pages, 5451 KB  
Article
EC-MFR: A Hierarchical Edge–Cloud Collaborative Framework for Multimodal Fact-Checking
by Hao Tao and Tao Chen
Information 2026, 17(5), 480; https://doi.org/10.3390/info17050480 - 13 May 2026
Viewed by 452
Abstract
The spread of multimodal misinformation demands verification that is both accurate and fast while keeping knowledge current. Large language models are powerful but costly and slow, and their static knowledge can lag behind events. We introduce EC-MFR, a hierarchical framework that divides work [...] Read more.
The spread of multimodal misinformation demands verification that is both accurate and fast while keeping knowledge current. Large language models are powerful but costly and slow, and their static knowledge can lag behind events. We introduce EC-MFR, a hierarchical framework that divides work between edge and the cloud. The system first optionally decomposes the claim into a few targeted sub-claims to guide retrieval, retrieves text and image evidence, and then compresses it into a small set of question–answer items using a lightweight, quantized multimodal language model deployed at the edge. A compact verifier on the edge predicts a label with calibrated confidence. If confidence is high, the decision is returned immediately. If confidence is low, the claim is sent to the cloud where retrieval can be expanded and the reasoning can be redone by a stronger verifier. This design offers three core benefits. It makes reasoning explicit through question–answer items, which shortens prompts and improves auditability. It improves retrieval recall via a light decomposition step that produces targeted sub-queries. Finally, it lets most easy claims finish on the edge to reduce cost and latency while preserving accuracy on difficult claims by allowing the cloud to broaden evidence and refine reasoning. Experiments on MOCHEG and AVERITEC validate the approach. Notably, EC-MFR achieves highly competitive accuracy of 54.10% on the multimodal MOCHEG dataset, and reaches 68.80% on AVERITEC under realistic retrieval settings, outperforming the GPT-4o cloud-only baseline by 6.6 percentage points. Furthermore, system-level profiling on edge hardware demonstrates that EC-MFR reduces processing costs by 51.8% and accelerates inference latency by 2.4× for edge-resolved claims, confirming a highly favorable accuracy–efficiency trade-off compared to existing multimodal fact-checking systems. We also formalize routing and efficiency and analyze calibration and retrieval. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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22 pages, 785 KB  
Article
Detection of Fake News in Romanian: LLM-Based Approaches to COVID-19 Misinformation
by Alexandru Dima, Ecaterina Ilis, Diana Florea and Mihai Dascalu
Information 2025, 16(9), 796; https://doi.org/10.3390/info16090796 - 13 Sep 2025
Cited by 4 | Viewed by 2548
Abstract
The spread of misinformation during the COVID-19 pandemic raised widespread concerns about public health communication and media reliability. In this study, we focus on these issues as they manifested in Romanian-language media and employ Large Language Models (LLMs) to classify misinformation, with a [...] Read more.
The spread of misinformation during the COVID-19 pandemic raised widespread concerns about public health communication and media reliability. In this study, we focus on these issues as they manifested in Romanian-language media and employ Large Language Models (LLMs) to classify misinformation, with a particular focus on super-narratives—broad thematic categories that capture recurring patterns and ideological framings commonly found in pandemic-related fake news, such as anti-vaccination discourse, conspiracy theories, or geopolitical blame. While some of the categories reflect global trends, others are shaped by the Romanian cultural and political context. We introduce a novel dataset of fake news centered on COVID-19 misinformation in the Romanian geopolitical context, comprising both annotated and unannotated articles. We experimented with multiple LLMs using zero-shot, few-shot, supervised, and semi-supervised learning strategies, achieving the best results with an LLaMA 3.1 8B model and semi-supervised learning, which yielded an F1-score of 78.81%. Experimental evaluations compared this approach to traditional Machine Learning classifiers augmented with morphosyntactic features. Results show that semi-supervised learning substantially improved classification results in both binary and multi-class settings. Our findings highlight the effectiveness of semi-supervised adaptation in low-resource, domain-specific contexts, as well as the necessity of enabling real-time misinformation tracking and enhancing transparency through claim-level explainability and fact-based counterarguments. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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16 pages, 889 KB  
Article
Human vs. AI: Assessing the Quality of Weight Loss Dietary Information Published on the Web
by Evaggelia Fappa, Mary Micheli, Dimitris Panaretos, Marios Skordis, Petroula Tsirpanli and George I. Panoutsopoulos
Information 2025, 16(7), 526; https://doi.org/10.3390/info16070526 - 23 Jun 2025
Cited by 2 | Viewed by 2049
Abstract
Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study [...] Read more.
Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study was to compare the quality assessment of online dietary weight loss information conducted by an AI assistant (ChatGPT 4.5) to that of health professionals. Thus, 177 webpages publishing dietary advice on weight loss were retrieved from the web and assessed by ChatGPT-4.5 and by dietitians through (1) a validated instrument (DISCERN) and (2) a self-made scale based on official guidelines for weight management. Also, webpages were assessed by a ChatGPT custom scoring system. Analysis revealed no significant differences in quantitative quality scores between human raters, ChatGPT-4.5, and the AI-derived system (p = 0.528). On the contrary, statistically significant differences were found between the three content accuracy scores (p < 0.001), with scores assigned by ChatGPT-4.5 being higher than those assigned by humans (all p < 0.001). Our findings suggest that ChatGPT-4.5 could complement human experts in evaluating online weight loss information, when using a validated instrument like DISCERN. However, more relevant research is needed before forming any suggestions. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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16 pages, 715 KB  
Article
Sentence Embeddings and Semantic Entity Extraction for Identification of Topics of Short Fact-Checked Claims
by Krzysztof Węcel, Marcin Sawiński, Włodzimierz Lewoniewski, Milena Stróżyna, Ewelina Księżniak and Witold Abramowicz
Information 2024, 15(10), 659; https://doi.org/10.3390/info15100659 - 21 Oct 2024
Cited by 2 | Viewed by 3647
Abstract
The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should [...] Read more.
The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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