Overview of ROMCIR 2026: The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval

• URL: http://arxiv.org/abs/2609.05684v1
• Date: Fri, 04 Sep 2026 19:37:00 GMT
• Title: Overview of ROMCIR 2026: The 6th Workshop on Reducing Online Misinformation through Credible Information Retrieval
• Abstract summary: Fake news wields power to sway public opinion on matters of politics and finance.<n>Deceptive reviews can bolster or tarnish the reputation of businesses.<n>There has been a surge of interest in various strategies to combat misinformation.
• Score: 4.902735109481777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance, wields power to sway public opinion on matters of politics and finance. Deceptive reviews can either bolster or tarnish the reputation of businesses, while unverified medical advice may steer people toward harmful health practices. In light of this challenging landscape, it has become imperative to ensure that users have access to both topically relevant and factually accurate information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat misinformation through different contexts and multiple tasks. The purpose of the ROMCIR Workshop, for some years now, is precisely that of engaging the Information Retrieval community to explore potential solutions that extend beyond conventional misinformation detection approaches. Key objectives include identifying subjective and objective factors associated with information credibility and truthfulness, respectively, and integrating such factors as fundamental dimensions of relevance within IR Systems (IRSs), achieving early detection of misinformation, and ensuring that the search results retrieved are not only truthful but also explainable to the users of IRSs. Moreover, it is essential to evaluate the role of generative models such as Large Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs, together with the contribution that the human-in-the-loop paradigm can have in this context.

Related papers

• Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions [16.02370545528885]
  Large Language Models (LLMs) have emerged as powerful tools that impact information integrity on social media platforms.<n>This comprehensive review examines the dual role of LLMs in both facilitating and mitigating various information integrity challenges.
  arXiv  Detail & Related papers  (2026-08-05T02:32:43Z)
• Decision-Making Amid Information-Based Threats in Sociotechnical Systems: A Review [0.0]
  Technological systems increasingly mediate human information exchange, spanning interactions among humans as well as between humans and artificial agents.<n>This review synthesizes insights from both domains to identify shared cognitive mechanisms that mediate vulnerability to information-based threats and shape behavioral outcomes.<n>We outline directions for future research aimed at integrating these perspectives, emphasizing the importance of such integration for mitigating human vulnerabilities and aligning human-machine representations.
  arXiv  Detail & Related papers  (2025-10-28T13:26:41Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic [12.873747057824833]
  We conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic.
  arXiv  Detail & Related papers  (2023-10-12T16:42:53Z)
• Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community [180.28262433004113]
  Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference. LLMs and humans form a new technical paradigm that is more powerful for information seeking. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023.
  arXiv  Detail & Related papers  (2023-07-19T05:23:43Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)
• A Modality-level Explainable Framework for Misinformation Checking in Social Networks [2.4028383570062606]
  This paper addresses automatic misinformation checking in social networks from a multimodal perspective. Our framework comprises a misinformation classifier assisted by explainable methods to generate modality-oriented explainable inferences.
  arXiv  Detail & Related papers  (2022-12-08T13:57:06Z)
• Overcoming Failures of Imagination in AI Infused System Development and Deployment [71.9309995623067]
  NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure" We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
  arXiv  Detail & Related papers  (2020-11-26T18:09:52Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.