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The Fourth Edition of the 2024 Workshop on Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2024) is part of the Satellite Events of the 46th European Conference on Information Retrieval (ECIR 2024). ROMCIR serves as a platform for discussions on accessing accurate information and addressing the issue of information disorder prevalent in today’s online landscape. The challenge is multifaceted, encompassing various types of information sources (e.g., websites, social media posts) across different platforms and domains (e.g., fake news detection, health-related information retrieval, propaganda reduction). Additionally, there is a critical need to assess the impact of generative models like Large Language Models (LLMs) on inadvertently amplifying misinformation and explore their potential role in supporting Information Retrieval Systems (IRSs). In this context, diverse approaches to the problem of access to truthful information are welcomed. Keynote speech and articles in this year’s workshop focus on themes such as health misinformation, multimedia and multimodal fact-checking, and information filtering to combat misinformation.

The PTOLEMY experiment aims at detecting the cosmic neutrino background, generated approximately one second after the Big Bang, in accordance with Standard Cosmology. Given the extremely low energy of these neutrinos, reliable experimental detection can be accomplished through neutrino captures on beta-unstable nuclides, eliminating the need for a specific energy threshold. Tritium implanted on a carbon-based nanostructure emerges as a promising candidate among the various isotopes due to its favorable cross-section and low-endpoint energy. The Ptolemy collaboration plans to integrate a solid-state tritium source with a novel compact electromagnetic filter, based on the dynamic transverse momentum cancellation concept. This filter will be employed in conjunction with an event-based preliminary radio-frequency preselection. The measurement of neutrino mass and the exploration of light sterile neutrinos represent additional outcomes stemming from the Ptolemy experiment’s physics potential, even when utilizing smaller or intermediate-scale detectors. To finalize the conceptualization of the detector, a demonstrator prototype will be assembled and tested at LNGS in 2024. This prototype aims at addressing the challenging aspects of the Ptolemy experiment.

Context. Zeeman-Doppler imaging (ZDI) is used to reconstruct the surface magnetic field of late-type stars from high-resolution spectropolarimetric observations. The results are usually described in terms of characteristics of the field topology, such as poloidality versus toroidality and axisymmetry versus non-axisymmetry, in addition to the field strength. Aims. In this study, we want to test how well these characteristics are preserved when applying the ZDI method to simulated data. We are particularly interested in how accurately the field topology is preserved and to what extent stellar parameters, such as projected rotation velocity and rotation axis inclination, influence the reconstruction. Methods. For these tests, we used published magnetic field vector data from direct numerical magnetohydrodynamic simulations taken near the surface of the simulation domain. These simulations have variable rotation rates and therefore represent different levels of activity of an otherwise Sun-like setup with a convective envelope of solar thickness. Our ZDI reconstruction is based on spherical harmonics expansion. By comparing the original values to those of the reconstructed images, we study the ability to reconstruct the surface magnetic field in terms of various characteristics of the field. Results. In general, the ZDI method works as expected. The main large-scale features are reasonably well recovered, but the strength of the recovered magnetic field is just a fraction of the original input. The quality of the reconstruction shows clear correlations with the data quality. Furthermore, there are some spurious dependencies between stellar parameters and the characteristics of the field. Conclusions. Our study uncovers some limits of ZDI. Firstly, the recovered field strength will generally be lower than the ‘real’ value, as smaller structures with opposite polarities will be blurred in the inversion. This is also seen in the relative distribution of magnetic energy in, QC 20240229

Recently, the Ethereum blockchain changed its consensus algorithm from Proof-of-Work (PoW) to Proof-of-Stake (PoS). This change has greatly reduced overall energy consumption but has paved the way for the profiling of numerous mechanisms and actors that can operate off-chain to achieve Maximal Extractable Value (MEV). This raises questions about how transparent such a scenario is, both from the point of view of block validation power and from the point of view of the rewards achieved by distinct actors within the platform. To address this concern and to mitigate potential negative externalities, a permissionless ecosystem has recently been proposed that should be transparent and fair for the extraction of MEV. In this article, after briefly describing the new ecosystem, we conduct an in-depth analysis of Ethereum blocks and other information about the actors operating in the ecosystem, to highlight potential critical situations.

We study dark matter, assumed to be composed by weak interacting massive particles (WIMPs), scattering off ${}^2$H and ${}^4$He nuclei. In order to parameterize the WIMP-nucleon interaction the chiral effective field theory approach is used. Considering only interactions invariant under parity, charge conjugation and time reversal, we examine five interaction types: scalar, pseudoscalar, vector, axial and tensor. Scattering amplitudes between two nucleons and a WIMP are determined up to second order of chiral perturbation theory. We apply this program to calculate the interaction rate as function of the WIMP mass and of the magnitude of the WIMP-quark coupling constants. From our study, we conclude that the scalar nuclear response functions result much greater than the others due to theirs large combination of low energy constants. We verify that the leading order contributions are dominant in this low energy processes. We also provide an estimate for the background due to atmospheric neutrinos.

This study provides insights into both addressing data confidentiality concerns and enhancing document retrieval effectiveness in Data Marketplaces, which in this specific study consist of unstructured, textual documents. Through a semi-automatic sanitization process leveraging token masking with text summarization, possibly complemented by Coreference Resolution, the proposed solution mitigates the risk of inferring confidential information while maintaining search performance. Experimental results demonstrate encouraging improvements in both aspects with respect to baseline solutions.

This study delves into the privacy risks associated with user interactions in complex networks such as those generated on social media platforms. In such networks, potentially sensitive information can be extracted and/or inferred from explicitly user-generated content and its (often uncontrolled) dissemination. Hence, this preliminary work first studies an unsupervised model generating a privacy risk score for a given user, which considers both sensitive information released directly by the user and content propagation in the complex network. In addition, a supervised model is studied, which identifies and incorporates features related to privacy risk. The results of both multi-class and binary privacy risk classification for both models are presented, using the Twitter platform as a scenario, and a publicly accessible purpose-built dataset.

The correlation function is a useful tool to study the interaction between hadrons. The theoretical description of this observable requires the knowledge of the scattering wave function, whose asymptotic part is distorted when two or more particles are charged. For a system of three (or more) particles, with more than two particles asymptotically free and at least two of them charged, the asymptotic part of the wave function is not known in a closed form. In the present study we introduce a screened Coulomb potential and analyze the impact of the screening radius on the correlation function. As we will show, when a sufficiently large screening radius is used, the correlation function results almost unchanged if compared to the case in which the unscreened Coulomb potential is used. This fact allows the use of free asymptotic matching conditions in the solution of the scattering equation simplifying noticeably the calculation of the correlation function. As an illustration we discuss the pp and ppp correlation functions.

In this paper, we propose a novel approach to consider multiple dimensions of relevance in cross-encoder re-ranking. On the one hand, cross-encoders constitute an effective solution for re-ranking when considering a single relevance dimension such as topicality, but are not designed to straightforwardly account for additional relevance dimensions. On the other hand, the majority of re-ranking models accounting for multdimensional relevance are often based on the aggregation of multiple relevance scores at the re-ranking stage, leading to potential compensatory effects. To address these issues, in the proposed solution we enhance the candidate documents retrieved by a first-stage lexical retrieval model with suitable relevance statements related to distinct relevance dimensions, and then perform a re-ranking on them with cross-encoders. In this work we focus, in particular, on an extra dimension of relevance beyond topicality, namely, credibility, to address health misinformation in the Consumer Health Search task. Experimental evaluations are performed by considering publicly available datasets; our results show that the proposed approach statistically outperforms state-of-the-art aggregation-based and cross-encoder re-rankers.

In the realm of the Social Web, we are continuously surrounded by information pollution, posing significant threats to both individuals and society as a whole. Instances of false news, for instance, wield the 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 truthful information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat disinformation 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 integrating information truthfulness as a fundamental dimension of relevance within Information Retrieval Systems (IRSs) and ensuring that truthful search results are also explainable to IRS users. Moreover, it is essential to evaluate the role of generative models such as Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs.