Misinformation and Its Impacts

Neueste Paper

• Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models

  Claudiu Creanga, Ioachim Lihor, Liviu P. Dinu · 5. Oktober 2026

  Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. However, identifying specific propaganda techniques presents a significant challenge due to their often subtle nature and reliance on context, making …

• How Robust Is Multimodal Claim Verification to LLM Rewriting?

  Yun-Ang Wu, Xanh Ho, Andre Greiner-Petter, Sunisth Kumar, Tian Cheng Xia, Florian Boudin, Akiko Aizawa · 5. Oktober 2026

  LLMs are known to introduce stylistic changes into generated text, yet how these stylistic shifts affect model decisions on scientific tasks remains underexplored. In this paper, we focus on multimodal claim verification, where the goal is to determine whether a textual claim is grounded in a given …

• Misinformation Without Triggers: From Factual Answers to Downstream Decisions

  Lin Tian, Marian-Andrei Rizoiu · 5. Oktober 2026

  Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and decisions that use it. Most data-poisoning studies add a trigger to the training data and activate it in the prompt. False documents can also change …

• VERITYGATE: A Four-Gate Schema-Level Faithfulness Framework and Paired Benchmark for Grounded LLM Narrations over Structured Evidence

  Sachin Gupta · 2. Oktober 2026

  Fluent LLM explanations may not follow the evidence from a structured system. We present VERITYGATE, a four-gate checker for declared evidence IDs, entities, numbers, and claim types. It checks a fixed schema; it does not verify every fact in the prose. At r=0 and r=1, we test 900 instances per sett…

• Crude, Commercial, and Self-Referential: Chinese-Language Coordinated Activity in Japanese-Language X

  Kei Ichikawa, Bruno T. Sugano, Genta Toya, Wu Qianyun, Yasuhiro Hashimoto, Masashi Toyoda, Naoki Yoshinaga, Kazutoshi Sasahara · 2. Oktober 2026

  Malicious coordination has long been regarded as a principal source of information ecosystem pollution. Here, we focus on crude, text-repetition-based coordination. As the demand for mitigating its dissemination has grown, scholars have studied such coordination, focusing especially on bot detection…

• Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram

  Panteleimon Tsagkarakis, Emmanouil Papadogiannakis, Evangelos Markatos · 2. Oktober 2026

  In recent years, disinformation has increasingly proliferated across social networks. The firing of fact-checkers from Meta and the disbanding of Twitter's Trust and Safety Council suggest that this trend will continue to escalate. While disinformation sources (e.g., social network accounts) can som…

• RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection

  Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhongjie Ba, Zhichao Lian · 1. Oktober 2026

  Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level retrieval and are therefore prone to introducing event-irrelevant noise. Meanwhile, existing methods mainly focus on improving overall performance and …

• Rethinking Multimodal Fake News Detection in the Generative AI Era

  Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhichao Lian · 1. Oktober 2026

  Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipulated material into complex forms in which native and generated content jointly participate. Existing multimodal fake news detection research primaril…

• Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law

  Bram Rijsbosch, Luka Bekavac, Henry Tari, Gijs van Dijck, Konrad Kollnig · 1. Oktober 2026

  AI labels are emerging as a primary safeguard for transparency about AI-generated content on social media, including under the EU Digital Services Act and AI Act. Yet, limited systematic evidence exists on how platforms implement such labels in practice. We conduct a legally grounded audit of AI lab…

• Fair Fact-Checking: Closing the Cross-Lingual Gap in LLM Factual Judgement with RoSh

  Muhammad Ahmad, Fatemeh Seyedin, Adrian Weller, Dongwon Lee, Mahmoudreza Babaei · 30. September 2026

  Misinformation on social media remains a critical problem, and more and more people settle it by asking a language model instead of a fact checker. Whether models judge such claims reliably is debated; whether they judge them equally well in every language people ask in has gone almost unasked. We t…

• MIC: Explaining Image-Claim Inconsistencies in AI-Generated Multimodal Misinformation

  Ruihong Zeng, Jonathan Tonglet, Preslav Nakov, Iryna Gurevych · 30. September 2026

  Claims paired with AI-generated images are a rapidly growing form of misinformation. Existing automated fact-checking (AFC) methods mainly treat this as a provenance problem, detecting low-level synthesis artifacts to decide whether an image is AI-generated. However, such methods do not verify what …

• Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

  Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang · 30. September 2026

  The rapid advancement of generative AI raises concerns about the misuse of Multimodal LLMs (MLLMs) for large-scale disinformation campaigns on social media. Despite existing research on textual disinformation, a fundamental question remains unanswered: can MLLMs be exploited to fabricate realistic m…

• Multi-Channel Mitigation of Source-Trust Shortcuts in Fact-Checking RL Agents

  Jianchang Su, Yiwei Yang, Wei Zhang · 30. September 2026

  Retrieval-augmented fact-checkers often receive a reliability label, such as HIGH or LOW trust, for each evidence source. These labels should adjust the model's confidence and its decision to search for more evidence, while the verdict should follow the evidence content. We introduce TrustSwap, a co…

• Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulations

  Azza Bouleimen, Nicol\`o Pagan, Anik\'o Hann\'ak · 30. September 2026

  Generative agent-based models (GABMs) are increasingly used to simulate social media dynamics, including misinformation spread. For such social simulations to be valid proxies of human behavior, LLM agents should replicate established human cognitive biases, among them the Illusory Truth Effect (ITE…

• OpenFC: Learning Verification Policies towards Open-Search Fact Checking

  Xinming Wang, Kaixiang Qiu, Yansong Lin, Chunji Lv, Yi Chen, Boran Wang, Hong-Ming Yang, Xu-Yao Zhang · 29. September 2026

  Open-search fact checking is not merely retrieval followed by classification, but a sequential decision problem in which every query, source visit, and stopping decision reshapes the evidence available for verification. Yet existing systems often distribute these decisions across predefined pipeline…

• Meme Template Identification in the Wild: Comparing Methods for Semi-Open-Set Recognition

  Huy Nguyen, Don\'at \'Akos K\"oller, Levente Murg\'as, J\'ozsef Pint\'er, Marcell Nagy, Kate Barnes, Roland Molontay · 29. September 2026

  Image-with-text memes are a dominant form of online communication, and much of their spread happens through meme templates which are recurring visual formats that users adapt with new text or imagery. Most prior work scores memes individually for engagement or harmful content, an approach that is st…

• VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation

  Hanxun Huang, Yutao Wu, Qizhou Wang, Silvia Monta\~na-Ni\~no, Yige Li, Xiang Zheng, Elif Buse Doyuran, Phoebe Matich, Xiao Liu, Xingjun Ma, Sarah Erfani, Christopher Leckie · 29. September 2026

  Large language models (LLMs) have made misinformation inexpensive to produce but not to verify, creating a growing asymmetry in the information ecosystem. Under tight time, labor, and budget constraints, media organizations, platforms, and fact-checkers rely on screening to prioritize which content …

• Perceived and Actual AI Deepfakes: The Case of Sudan

  Eilaf Mohamed · 29. September 2026

  Sudan's AI deepfake risk currently appears driven more by demand-side vulnerabilities than the volume of AI deepfake content. While AI-generated disinformation in Sudanese feeds remains limited, perceived (alleged) AI deepfakes and general AI skepticism worsen the situation and contribute to general…

• MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

  Peipei Li, Shuhan Xia, Shengyang Liu, Zekun Li, Ran He · 28. September 2026

  Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented methods rely on predefined workflows or inference-time planning, limiting adaptability or increasing inference cost. To address this issue, we introduce …

• Evaluating Sycophancy in Chinese Large Language Models on Factual Questions Derived from Online Search Queries

  Geng Liu, Feng Li, Mengxiao Zhu, Francesco Pierri · 28. September 2026

  As large language models increasingly mediate information access, factually accurate and independent answers are critical. However, these models can exhibit sycophancy by aligning their responses with users' stated beliefs even when those beliefs are incorrect, potentially presenting misinformation …

• Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos

  Yuya Wake, Sho Tsugawa, Toshiyuki Amagasa · 28. September 2026

  Large language models (LLMs) are increasingly used to assess long-form medical videos, but their effectiveness may depend on whether transcripts are provided in full or compressed through summarization, retrieval, or claim screening. This study examines how such transcript compression affects LLM-ba…

• What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study

  Akshit Sharma, Prashant W. Patil · 28. September 2026

  Multimodal misinformation is increasingly crafted to look convincing by pairing a textual claim with an image that appears to "prove" it. Yet in practice, building effective detectors often hinges on a small set of design choices that are rarely examined in a controlled way. In this paper, we conduc…

• A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models

  Fanji Yang (Guizhou University of Finance and Economics), Huiyao Chen (Harbin Institute of Technology), Xi Yu (Guizhou University of Finance and Economics), Meishan Zhang (Harbin Institute of Technology), Xiaohong Xiao (Guizhou University of Commerce), Mingsen Deng (Guizhou University of Finance and Economics) · 28. September 2026

  Online reviews shape consumer decisions, platform governance, and corporate reputation.Fake reviews compromise this information channel by injecting deceptive evidence into rating systems, recommendation pipelines, and public trust mechanisms.The rise of large language models, or LLMs, has changed t…

• Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation

  Zhaoyang Cao, Miriam Metzger, Reza Zafarani · 28. September 2026

  Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed computational framewor…

• Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X

  Pan Li, Shuang Gao · 25. September 2026

  Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X's recommendation algorithms, we conduct wh…