Information Extraction (IE) is a key task in Natural Language Processing (NLP) that transforms unstructured text into structured data. This study compares human annotation, rule-based systems, and Large Language Models (LLMs) for domain-specific IE, focusing on real estate auction documents. We assess each method in terms of accuracy, scalability, and cost-efficiency, highlighting the associated trade-offs. Our findings provide valuable insights into the effectiveness of using LLMs for the considered task and, more broadly, offer guidance on how organizations can balance automation, maintainability, and performance when selecting the most suitable IE solution.

Abstract The exponential surge in online health information, coupled with its increasing use by non-experts, highlights the pressing need for advanced Health Information Retrieval (HIR) models that consider not only topical relevance but also the factual accuracy of the retrieved information, given the potential risks associated with health misinformation. To this aim, this paper introduces a solution driven by Retrieval-Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs) to enhance the retrieval of health-related documents grounded in scientific evidence. In particular, we propose a three-stage model: in the first stage, the user’s query is employed to retrieve topically relevant passages with associated references from a knowledge base constituted by scientific literature. In the second stage, these passages, alongside the initial query, are processed by LLMs to generate a contextually relevant rich text (GenText). In the last stage, the documents to be retrieved are evaluated and ranked both from the point of view of topical relevance and factual accuracy by means of their comparison with GenText, either through stance detection or semantic similarity. In addition to calculating factual accuracy, GenText can offer a layer of explainability for it, aiding users in understanding the reasoning behind the retrieval. Experimental evaluation of our model on benchmark datasets and against baseline models demonstrates its effectiveness in enhancing the retrieval of both topically relevant and factually accurate health information, thus presenting a significant step forward in the health misinformation mitigation problem.

Social media platforms have become central arenas for public discourse, enabling the exchange of ideas and information among diverse user groups. However, the rise of echo chambers, where individuals reinforce their existing beliefs through repeated interactions with like-minded users, poses significant challenges to the democratic exchange of ideas and the potential for polarization and information disorder. This article presents a comparative analysis of the main metrics that have been proposed in the literature for echo chamber detection, with a focus on their application in a cross-platform scenario constituted by the two major social media platforms, i.e., Twitter (now renamed \(\mathbb {X}\) ) and Reddit. The echo chamber detection metrics considered encompass network analysis, content analysis, and hybrid solutions. The findings of this work shed light on the unique dynamics of echo chambers present on the two social media platforms, while also highlighting the strengths and limitations of various metrics employed to identify them, and their transversality to the different social graph modeling and domains considered.

The Social Web represents nowadays the principal means to support and foster social interactions among people through Web 2.0 technologies. Individuals interact in virtual communities to pursue mutual interests or goals, by exchanging multiple kinds of contents (i.e., textual, acoustic, visual), the so-called User-Generated Content (UGC). In this context, the SeCredISData Special Session is especially devoted at discussing the implications that the analysis of big social data has in tackling open issues related to society from different perspectives. On one side, there is the need to push forward the research on emotion and sentiment, and the investigation of affective cognitive models and their possible integration into intelligent systems. On the other side, it is urgent to address the issue of on-line information credibility assessment, in an era where trusted intermediaries have disappeared and people must rely only on their cognitive capacities to judge information. The Special Session is therefore aimed at promoting the development of models and applications able to tackle these issues.

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

The Social Web supports and fosters social interactions by means of different social media, which allow the spread of the so called User-Generated Content (UGC). In this context, characterized by the absence of trusted third parties that verify the reliability of the sources and the believability of the content generated, the issue of assessing the credibility of the information diffused by means of social media is receiving increasing attention. In the literature, this issue has been mainly tackled as a classification problem; information is categorized into genuine and fake, usually by implementing or applying classifiers that consider multiple kinds of features (mainly textual and non-textual) to be evaluated in terms of credibility.

This article presents an approach for the identification of potential new occupations, i.e., professions, not yet codified by the international standard taxonomy ISCO. This work is framed within the research activities of the WoLMIS project, developed by the University of Milano-Bicocca for the CEDEFOP European Agency, which classifies on-line job offers according to the ISCO taxonomy by using machine learning techniques.

The Social Web is characterized by a massive diffusion of unfiltered content, directly generated by users via the spread of different social media platforms. In this context, a challenging issue is to assess the veracity of the information generated within the sites of online reviews. To address this issue, a common practice in the literature is to select and analyze some veracity features associated with users and their reviews, by mostly applying machine learning techniques, to provide a classification in genuine and deceptive reviews. In this paper, we do not focus on the feature selection and user behavior analysis issues, but we concentrate on the aggregation process with respect to each single veracity feature. In most of the approaches based on machine learning techniques, the contribution of each feature in the classification process is not measurable by the user. For this reason, we propose a multicriteria decision making approach based both on the assessment of multiple criteria and the use of aggregation operators with the aim of obtaining a veracity score associated with each review. Based on this score, it is possible to detect fake reviews. The proposed model is evaluated on a Yelp data set by applying different aggregation schemes, and it is compared with well-known supervised machine learning techniques.

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run