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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

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

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

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

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

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.

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

Introduction: People are today increasingly relying on health information they find online to make decisions that may impact both their physical and mental wellbeing. Therefore, there is a growing need for systems that can assess the truthfulness of such health information. Most of the current literature solutions use machine learning or knowledge-based approaches treating the problem as a binary classification task, discriminating between correct information and misinformation. Such solutions present several problems with regard to user decision making, among which: (i) the binary classification task provides users with just two predetermined possibilities with respect to the truthfulness of the information, which users should take for granted; indeed, (ii) the processes by which the results were obtained are often opaque and the results themselves have little or no interpretation. Methods: To address these issues, we approach the problem as an ad hoc retrieval task rather than a classification task, with reference, in particular, to the Consumer Health Search task. To do this, a previously proposed Information Retrieval model, which considers information truthfulness as a dimension of relevance, is used to obtain a ranked list of both topically-relevant and truthful documents. The novelty of this work concerns the extension of such a model with a solution for the explainability of the results obtained, by relying on a knowledge base consisting of scientific evidence in the form of medical journal articles. Results and discussion: We evaluate the proposed solution both quantitatively, as a standard classification task, and qualitatively, through a user study to examine the “explained” ranked list of documents. The results obtained illustrate the solution's effectiveness and usefulness in making the retrieved results more interpretable by Consumer Health Searchers, both with respect to topical relevance and truthfulness.

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

At the time of writing, nearly four billion people worldwide employ social media platforms such as Facebook, Instagram, WeChat, TikTok, etc. to share content of various kinds, which may also include personal data. In addition to this, users interact with members of the virtual community, leaving behind important behavioral traces. In most cases, people do not have a full understanding of who will be able to access and use such a body of information, and for what purposes. Although social platforms provide users with some tools to protect their privacy, the very nature of these technologies and the psychological characteristics of users often lead them to ignore such solutions. To address this issue, in this paper we aim to propose a model for assessing the privacy of users on social media by identifying the critical aspects associated with their content and interactions generated on such platforms. This model, in particular, considers distinct features, of different kinds, that capture the level of users' exposure with respect to privacy. These features, dropped into a vector space, are used to derive a score that expresses, in a measurable way, the privacy risk of users compared to the information available on social media about them. The proposed model is instantiated and tested on data collected from the microblogging platform Twitter, on which the results of the experimental evaluation are analyzed. Specifically, the model is tested by considering both a binary scenario, i.e., where users' privacy is evaluated as at risk or not, a multi-class scenario, i.e., where their privacy is evaluated against different risk ranges, and a ranking scenario, i.e., where the users are ranked according to their privacy assessment.

The 2022 Workshop on Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2022), at its Second Edition as part of the Satellite Events of the 44th European Conference on Information Retrieval (ECIR 2022), is concerned with providing users with access to genuine information, to mitigate the information disorder phenomenon characterizing the current online environment. This problem is very broad, as it concerns different information objects (e.g., Web pages, online accounts, social media posts, etc.) on different platforms, and different domains and purposes (e.g., detecting fake news, retrieving genuine healthrelated information, reducing propaganda and hate-speech, etc.). In this context, all those approaches that can serve, from different perspectives, to tackle the genuine information access problem, find their place. In particular, this year articles have been submitted that addressed the problem of preventing access to health misinformation and assessing the genuineness of multi-modal information.

With regard to the issue of access to health-related content circulating online, especially by laypersons, this article aims at illustrating the effectiveness of using features of a different nature in combination with machine learning and deep learning classifiers for the task of health misinformation detection. To this end, and for evaluation purposes, publicly available datasets consisting of health-related information in the form of both Web pages and social media content are considered.

In domain-specific search, effectiveness depends on considering multiple dimensions of relevance, beyond topicality, related to the domain involved. Estimating additional relevance dimensions can affect efficiency because the computation of relevance scores is time-consuming if performed on the full document, especially for query-dependent dimensions. Hence, this article introduces an approach for improving effectiveness in domain-specific search by considering multiple dimensions of relevance, namely topicality, correctness, and credibility. To address efficiency, we propose a re-ranking approach that estimates domain-specific relevance scores on document summaries, rather than full documents. We validate the proposed solution by performing the Ad-Hoc Retrieval task from the TREC 2020 Health Misinformation Track, a domain that crucially relies on the considered relevance dimensions. Our findings underscore the potential of our approach with respect to both effectiveness and efficiency.

For the first time, we approach in this work the problem of the renormalization of the Gamow-Teller decay operator for nuclear shell-model calculations by way of many-body perturbation theory, starting from a nuclear Hamiltonian and electroweak currents derived consistently by way of the chiral perturbation theory. These are the inputs we need to construct microscopically the effective shell-model Hamiltonians and decay operators. The goal is to assess the role of both electroweak currents and many-body correlations as the origins of the well-known problem of the quenching of the axial coupling constant gA. To this end, the calculation of observables related to the Gamow-Teller transitions has been performed for several nuclear systems outside the 40Ca and 56Ni closed cores and compared with the available data., Comment: 15 pages, 13 figures, 3 tables, to be published in Physical Review C

On September 15, 2022, Ethereum changed its consensus algorithm, moving from Proof-of-Work (PoW) to Proof-of-Stake (PoS). This event is commonly known as “The Merge”. While this change has considerably reduced the overall energy consumption, it has been observed that 40% of the first 1,000 blocks since then have been validated by a very limited amount of cryptocurrency holders. This raises serious questions about the decentralized nature of the system. To address this concern, and to mitigate the potential negative externalities of current Maximal Extractable Value (MEV) extraction strategies, the community has agreed to implement a permissionless, transparent, and fair ecosystem for MEV extraction. In this paper, after describing the new ecosystem, we perform an analysis of the blocks validated after Ethereum's “The Merge”. In particular, we analyze the actual distribution of validators by accounting for their activity both inside and outside the MEV ecosystem, and discuss how the scenario has changed in recent months.

Fake news is a major challenge in social media, particularly in the health domain where it can lead to severe consequences for both individuals and society as a whole. To contribute to combating this problem, we present a novel solution for improving the accuracy of detecting fake health news, utilizing a fine-Tuned BERT model that integrates both user-and content-related socio-contextual information. Specifically, this information is combined with the textual content itself to form a socio-contextual input sequence for the BERT model. By fine-Tuning such a model with respect to the health misinformation detection task, the resulting classifier can accurately predict the category to which each piece of content belongs, i.e., either "real health news"or "fake health news". We validate our solution through a series of experiments conducted on distinct publicly available datasets constituted by health-related tweets. These results illustrate the superiority of the proposed solution compared to the standard BERT baseline model and other advanced models. Indeed, they show that the integration of socio-contextual information in the detection process positively contributes to increasing the overall accuracy of the fake health news detection task. The study also suggests, in a preliminary way, how such information could be used for the explainability of the model itself.

Searching for online information is nowadays a critical task in a scenario characterized by information overload and misinformation. To address these issues, it is necessary to provide users with both topically relevant and truthful information. Re-ranking is a strategy often used in Information Retrieval (IR) to consider multiple dimensions of relevance. However, re-rankers often analyze the full text of documents to obtain an overall relevance score at the re-ranking stage, which can lead to sub-optimal results. Some recent Transformer-based re-rankers actually consider text passages rather than the entire document, but focus only on topical relevance. Transformers are also being used in non-IR solutions to identify information truthfulness, but just to perform a binary classification task. Therefore, in this article, we propose an IR model based on re-ranking that focuses on suitably identified text passages from documents for retrieving both topically relevant and truthful information. This approach significantly reduces the noise introduced by query-unrelated content in long documents and allows us to evaluate the document’s truthfulness against it, enabling more effective retrieval. We tested the effectiveness of the proposed solution in the context of the Consumer Health Search task, considering publicly available datasets. Our results show that the proposed approach statistically outperforms full-text retrieval models in the context of multidimensional relevance, such as those based on aggregation, and monodimensional relevance Transformer-based re-rankers, such as BERT-based re-rankers.

With the advent of the Social Web, we are constantly and more than ever assaulted by different kinds of information pollution, which may lead to severe issues for both individuals and society as a whole. In this context, it becomes essential to guarantee users access to genuine information that does not distort their perception of reality. For this reason, in recent years, numerous approaches have been proposed for the identification of misinformation, in different contexts and for different purposes. However, the problem has not yet been sufficiently addressed in the field of Information Retrieval, because it has been treated primarily as a classification task to identify information versus misinformation. Hence, the purpose of this Workshop is to address the IR community for solutions in which, among other issues, the genuineness of information is considered as one of the dimensions of relevance within search engines or recommender systems, early detection of misinformation can be achieved, the results obtained are explainable with respect to the users of Information Retrieval Systems, user’s privacy is taken into consideration.

Correlation functions of hadrons can be accessed in high-energy collisions of atomic nuclei, revealing information about the underlying interaction. This work complements experimental efforts to study nucleon-deuteron $Nd$ -- with $N=p$ (proton) or $N=n$ (neutron) -- correlations with theory evaluations using different techniques. The correlation functions $C_{nd}$ and $C_{pd}$ are calculated based on scattering wave function, extending previous benchmarks for the $Nd$ scattering matrix to this new observable. We use hyperspherical harmonics and Faddeev techniques with one of the widely used nucleon-nucleon ($NN$) interactions, the Argonne $v_{18}$ potential. Moreover, in the low-energy region we perform additional calculations in the framework of pionless effective field theory. The $pd$ correlation function is computed in the large-energy region to make contact with a recent measurement by the ALICE Collaboration. We show that the scattering wave function has the proper dynamical input to describe an initial rise and subsequent oscillations of $C_{pd}$ as a function of the energy. Effects on the observables using different $NN$ and three-nucleon potentials are evaluated with the conclusion that variations of around $2\%$ are observed. Although these effects are small, future measurements can go beyond this accuracy allowing for new detailed studies of strong interaction in light nuclear systems. The present study supports the current efforts devoted to the measurement of correlation functions in systems dominated by the strong interactions, such as $pd$, $ppp$, $\Lambda d$ and $pp\Lambda$., Comment: 23 pages, 8 figures

The increasing availability of online data has meant that data-driven models have been applied to more and more tasks in recent years. In some domains and/or applications, such data must be protected before they are used. Hence, one of the problems only partially addressed in the literature is to determine how the performance of Machine Learning models is affected by data protection. More important, the explainability of the results of such models as a consequence of data protection has been even less investigated to date. In this paper, we refer to this very problem by considering non-perturbative data protection, and by studying the explainability of supervised models applied to the data classification task.

In this paper, we provide an overview of the ninth annual edition of the CLEF eHealth evaluation lab. CLEF eHealth 2021 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, health care professionals, and health scientists in understanding, accessing, and authoring electronic health information in a multilingual setting. The 2021 lab offered two tasks: Task 1 on multilingual Information Extraction (IE), this year extending to a corpus of Spanish radiology reports; and Task 2 on Consumer Health Search (CHS) that builds on the previous year’s Information Retrieval (IR) tasks. In total, 11 teams took part in these tasks (7 in Task 1 on IE and 4 in Task 2 on IR). Herein, we describe the resources created for these tasks and the evaluation methodology adopted, and we provide a brief summary of the participants of this year’s challenges as well as the results obtained. As in previous years, the organizers have made data, tools, and more specific overview papers associated with the lab tasks available for future research and development.

Social media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

Faced with the problem of widespread health misinformation, in recent years credibility is being considered as one of the important dimensions for health-related information access and retrieval. To encourage research in this field, a couple of evaluation labs have been recently set up to provide large test collections, baselines, and evaluation metrics to interested researchers. The purpose of this article is to provide an overview of such evaluation labs, discussing their characteristics and open issues.

In recent years, deep learning models have been successfully applied to Information Retrieval (IR), mainly for assessing the topical relevance of documents with respect to queries. However, relevance is a multidimensional concept, which can be assessed based on several criteria, depending on the document type, the domain considered, the search task performed, etc. Given that recent advancements in deep neural networks enable several learning tasks to be solved simultaneously, in this paper we examine the possibility of modeling multidimensional relevance by jointly solving a retrieval task, to learn topical relevance, and a classification task, to learn additional relevance dimensions. To instantiate and evaluate the proposed model, we consider three query-independent relevance dimensions beyond topicality, i.e., readability, trustworthiness, and credibility. The reported findings show that the proposed joint modeling can improve the performance of the retrieval task.

Motivated by the ever increasing difficulties faced by laypeople in retrieving and digesting valid and relevant information to make health-centred decisions, the CLEF eHealth lab series has offered shared tasks to the community in the fields of Information Extraction (IE), management, and Information Retrieval (IR) since 2013. These tasks have attracted large participation and led to statistically significant improvements in processing quality. In 2021, CLEF eHealth is calling for participants to contribute to the following two tasks: Task 1 on IE focuses on IE from noisy text. Participants will identify and classify Named Entities in written ultrasonography reports, containing misspellings and inconsistencies, from a major public hospital in Argentina. Identified entities will then have to be classified, which can be very challenging as it requires to handle lexical variations. Task 2 is a novel extension of the most popular and established task on consumer health search (CHS), aiming at retrieving relevant, understandable, and credible information for patients and their next-of-kins. In this paper we describe recent advances in the fields of IE and IR, and the subsequent offerings of this years CLEF eHealth lab challenges.

This paper details materials, methods, results, and analyses of the Consumer Health Search Task of the CLEF eHealth 2021 Evaluation Lab. This task investigates the effectiveness of information retrieval (IR) approaches in providing access to medical information to laypeople. For this a TREC-style evaluation methodology was applied: a shared collection of documents and queries is distributed, participants' runs received, relevance assessments generated, and participants' submissions evaluated. The task generated a new representative web corpus including web pages acquired from a 2021 CommonCrawl and social media content from Twitter and Reddit, along with a new collection of 55 manually generated layperson medical queries and their respective credibility, understandability, and topicality assessments for returned documents. This year's task focused on three subtask: (i) ad-hoc IR, (ii) weakly supervised IR, and (iii) document credibility prediction. In total, 15 runs were submitted to the three subtasks: eight addressed the ad-hoc IR task, three the weakly supervised IR challenge, and 4 the document credibility prediction challenge. As in previous years, the organizers have made data and tools associated with the task available for future research and development.

In this paper, we provide an overview of the CLEF eHealth Task 2 on Information Retrieval (IR), organized as part of the eighth annual edition of the CLEF eHealth evaluation lab by the Conference and Labs of the Evaluation Forum. Its aim was to address laypeople’s difficulties in retrieving and digesting valid and relevant information, in their preferred language, to make health-centred decisions. The task was a novel extension of the most popular and established task in CLEF eHealth on Consumer Health Search (CHS), which makes responses to spoken ad-hoc queries. In total, five submissions were made to its two subtasks; three addressed the ad-hoc IR task on text data and two considered the spoken queries. Herein, we describe the resources created for the task and evaluation methodology adopted. We also summarize lab submissions and results. As in previous years, organizers have made data, methods, and tools associated with the lab tasks available for future research and development.

While the phenomenon of gluon nuclear shadowing at small $x$ has been getting confirmation in QCD analyses of various LHC measurements involving heavy nuclei, it has not been possible so far to establish experimentally the number of target nucleons responsible for nuclear shadowing in a given process. To address this issue, we study coherent $J/\psi$ electroproduction on $^4$He and $^3$He in the kinematics of a future electron-ion collider (EIC). Using an accurate treatment of the wave functions of $^4$He and $^3$He, we show that this process has the power to disentangle the contributions of the interaction with a specific number of nucleons, in particular, the contribution of two nucleons at the momentum transfer $t \neq 0$. This challenging, first ever calculation of coherent vector meson production on light nuclei, using realistic nuclear wave functions, provides an additional test of models of nuclear shadowing and a novel probe of the gluon distribution in impact parameter representation, allowing for a 3D imaging of gluons in light nuclei. Besides, thanks to this analysis, unique information on the real part of the corresponding scattering amplitude could be accessed., Comment: 6 pages, 5 figures

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the $\beta$-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised in [Cheipesh et al., Phys. Rev. D 104, 116004 (2021)], and discuss the relevant time scales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We propose an experimental signature to test this theoretical understanding. We also perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty., Comment: 13 pages, 5 figures

While the phenomenon of gluon nuclear shadowing at small $x$ has been getting confirmation in QCD analyses of various LHC measurements involving heavy nuclei, it has not been possible so far to establish experimentally the number of target nucleons responsible for nuclear shadowing in a given process. To address this issue, we study coherent $J/\psi$ electroproduction on $^4$He and $^3$He in the kinematics of a future electron-ion collider (EIC). Using an accurate treatment of the wave functions of $^4$He and $^3$He, we show that this process has the power to disentangle the contributions of the interaction with a specific number of nucleons, in particular, the contribution of two nucleons at the momentum transfer $t \neq 0$. This challenging, first ever calculation of coherent vector meson production on light nuclei, using realistic nuclear wave functions, provides an additional test of models of nuclear shadowing and a novel probe of the gluon distribution in impact parameter representation, allowing for a 3D imaging of gluons in light nuclei. Besides, thanks to this analysis, unique information on the real part of the corresponding scattering amplitude could be accessed., Comment: 6 pages, 5 figures

The PTOLEMY transverse drift filter is a new concept to enable precision analysis of the energy spectrum of electrons near the tritium beta-decay endpoint. This paper details the implementation and optimization methods for successful operation of the filter for electrons with a known pitch angle. We present the first demonstrator that produces the required magnetic field properties with an iron return-flux magnet. Two methods for the setting of filter electrode voltages are detailed. The challenges of low-energy electron transport in cases of low field are discussed, such as the growth of the cyclotron radius with decreasing magnetic field, which puts a ceiling on filter performance relative to fixed filter dimensions. Additionally, low pitch angle trajectories are dominated by motion parallel to the magnetic field lines and introduce non-adiabatic conditions and curvature drift. To minimize these effects and maximize electron acceptance into the filter, we present a three-potential-well design to simultaneously drain the parallel and transverse kinetic energies throughout the length of the filter. These optimizations are shown, in simulation, to achieve low-energy electron transport from a 1 T iron core (or 3 T superconducting) starting field with initial kinetic energy of 18.6 keV drained to < 10 eV (< 1 eV) in about 80 cm. This result for low field operation paves the way for the first demonstrator of the PTOLEMY spectrometer for measurement of electrons near the tritium endpoint to be constructed at the Gran Sasso National Laboratory (LNGS) in Italy.

Contains fulltext : 283443.pdf (Publisher’s version ) (Open Access)

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the $\beta$-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised in [Cheipesh et al., Phys. Rev. D 104, 116004 (2021)], and discuss the relevant time scales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty., Comment: 14 pages, 5 figures

Contains fulltext : 283443.pdf (Publisher’s version ) (Open Access)

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the beta-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised by Cheipesh, Cheianov, and Boyarsky [Phys. Rev. D 104, 116004 (2021)], and discuss the relevant timescales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty.

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the beta-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised by Cheipesh, Cheianov, and Boyarsky [Phys. Rev. D 104, 116004 (2021)], and discuss the relevant timescales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty.

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the beta-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised by Cheipesh, Cheianov, and Boyarsky [Phys. Rev. D 104, 116004 (2021)], and discuss the relevant timescales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty.

We discuss the consequences of the quantum uncertainty on the spectrum of the electron emitted by the beta-processes of a tritium atom bound to a graphene sheet. We analyze quantitatively the issue recently raised by Cheipesh, Cheianov, and Boyarsky [Phys. Rev. D 104, 116004 (2021)], and discuss the relevant timescales and the degrees of freedom that can contribute to the intrinsic spread in the electron energy. We perform careful calculations of the potential between tritium and graphene with different coverages and geometries. With this at hand, we propose possible avenues to mitigate the effect of the quantum uncertainty.

Purpose: The purpose of this article is to explore how participants with different motivations (educational or leisure), familiarity with the medium (newbies and active Twitter users), and participating instructions respond to a highly structured digital social reading (DSR) activity in terms of intensity of engagement and social interaction. Design/methodology/approach: A case study involving students and teachers of 211 Italian high school classes and 242 other Twitter users, who generated a total of 18,962 tweets commenting on a literary text, was conducted. The authors performed both a quantitative analysis focusing on the number of tweets/retweets generated by participants and a network analysis exploiting the study of interactions between them. The authors also classified the tweets with respect to their originality, by using both automated text reuse detection approaches and manual categorization, to identify quotations, paraphrases and other forms of reader response. Findings: The decoupling (both in space and time) of text read (in class) and comments (on Twitter) likely led users to mainly share text excerpts rather than original personal reactions to the story. There was almost no interaction outside the classroom, neither with other students nor with generic Twitter users, characterizing this project as a shared experience of “audiencing” a media event. The intensity of social interactions is more related to the breadth of the audience reached by the user-generated content and to a strong retweeting activity. In general, better familiarity with digital (social) media is related to an increase in the level of social interaction. Originality/value: The authors analyzed one of the largest educational social reading projects ever realized, contributing to the still scarce empirical research about DSR. The authors employed state-of-the-art automated text reuse detection to classify reader response.