Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Abstract Named Entity Recognition (NER) in specialized domains like biomedicine suffers from acute data scarcity, requiring expensive expert annotations. While data augmentation offers a promising solution, it inevitably introduces noisy and mislabeled samples that can degrade model performance. This problem is amplified in few-shot scenarios where every training example matters. We introduce PALAUNER (Policy-based Active Learning to Augment Named Entity Recognition), a reinforcement learning framework that learns to select high-quality samples from augmented data pools. Using a deep Q-network, our agent evaluates samples based on content features and model predictions, deciding which examples will improve NER performance. Experiments across five BioNER benchmarks demonstrate that PALAUNER consistently enhances diverse augmentation methods, from simple perturbations to GPT-based generation. Average F1 improvements are of 0.5−7.1 points in few-shot settings. PALAUNER’s modular design enables seamless integration with emerging augmentation techniques, providing a generalizable solution for training data quality enhancement. We publicly release our code on GitHub: ( https://github.com/picuslab/palauner ).

Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER

Biomedical misinformation - ranging from misleading social media posts to fake news articles and deepfake videos - is increasingly pervasive across digital platforms, posing significant risks to public health and clinical decision-making. We developed CER, a comprehensive fact-checking system designed specifically for biomedical content. CER integrates specialized components for claim detection, scientific evidence retrieval, and veracity assessment, leveraging both transformer models and large language models to process textual and multimedia content. Unlike existing fact-checking systems that focus solely on structured text, CER can analyze claims from diverse sources including videos and web content through automatic transcription and text extraction. The system interfaces with PubMed for evidence retrieval, employing both sparse and dense retrieval methods to gather relevant scientific literature. Our evaluations on standard benchmarks including HealthFC, BioASQ-7, and SciFact demonstrate that CER achieves state-of-the-art performance, with F1-score improvements compared to existing approaches. To ensure reproducibility and transparency, we release the GitHub repository with the source code, within which you can reach an interactive demonstration of the system published on HuggingFace and a video demonstration of the system \https://github.com/PRAISELab-PicusLab/CER-Fact-Checking.

The spread of misinformation and fake news on digital platforms poses significant societal challenges, underscoring the need for robust detection. Multi-task learning leverages relationships among disinformation-related tasks (e.g., stance detection, rumor classification) to enhance detection; however, it risks negative transfer , potentially degrading performance instead of achieving positive transfer . In this paper, we systematically investigate the mechanisms underlying positive and negative transfers across a comprehensive set of disinformation-related tasks, including Sentiment Analysis (SA), Fake News Detection (FND), Stance Detection (SD), and Topic Detection (TD). Specifically, we pioneer the use of explanations to uncover the differences between models trained under single-task and multi-task settings. Our results reveal instances of positive transfer across several task combinations, with multi-task learning yielding performance improvements of 3.26%, 6.57%, and 0.62% for SA, FND and TD tasks, respectively. Furthermore, when comparing explanations of single-task and multi-task models, we find that positive transfer refines the knowledge that can already be learnt in single-task settings by incorporating additional patterns from other tasks. Conversely, negative transfer significantly undermines models' knowledge to the extent that their explanations are equivalent to a random perturbation of the explanations generated by their single-task counterparts.

Geopolitical news provides vast amounts of information essential for understanding international relations and political events. However, organizing this information into a coherent, structured format poses challenges due to the complexity and dynamic nature of the domain. This paper introduces a scalable system leveraging Large Language Models to build continuously updated Knowledge Graphs from Italian geopolitical news. The system features a modular architecture, including a Collector Node for scalable article extraction, a Redis-based reliable queue to manage large-scale data ingestion, and a Named Entity Recognition/Relation Extraction Engine to standardize entity-relation triples. The framework addresses key challenges, such as continuous updating and hallucination mitigation, ensuring the reliability of the graph. Our evaluations demonstrate significant improvements in scalability, uniformity of extracted triples, and graph accuracy, making this architecture particularly suitable for real-time geopolitical analysis.

Legal Artificial Intelligence has emerged as an essential field, focusing on AI technologies that facilitate various legal tasks and alleviate the workload of legal professionals. Despite advancements in Legal Artificial Intelligence, there remains a critical gap in systems that can provide both comprehensive and contextually accurate retrieval tailored to the intricate structure of EU legislation. We propose EuropeanLawAdvisor, an efficient and user-friendly legal information retrieval system designed to deliver tailored responses to legal queries. This system utilizes open-source Large Language Models within a Retrieval-Augmented Generation framework, facilitating precise and relevant information retrieval. The system employs a robust retrieval approach that integrates multi-match, k-nearest neighbors, hybrid methods, and TF-IDF search strategies across both complete documents and segmented text indexes, ensuring comprehensive retrieval for diverse query types. The implementation of the framework has demonstrated significant improvements in the accuracy and relevance of responses to EU legal queries, enhancing both the retrieval of relevant legal documents and the generation of precise responses. We show that EuropeanLawAdvisor, leveraging open-source models like Phi3-mini-3B and LLaMa-3-8B, achieves competitive Faithfulness and Relevance compared to GPT-4-Turbo. The performance gap narrows significantly in zero-shot scenarios, and our approach outperforms GPT-4-Turbo in the percentage of answered questions. We publicly release our code on GitHub: https://github.com/raffaele-russo/EuropeanLawAdvisor.

Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of European Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms.

Recent years have seen an exponential growth (+98% in 2022 w.r.t. the previous year) of the number of research articles in the few-shot learning field, which aims at training machine learning models with extremely limited available data. The research interest toward few-shot learning systems for Named Entity Recognition (NER) is thus at the same time increasing. NER consists in identifying mentions of pre-defined entities from unstructured text, and serves as a fundamental step in many downstream tasks, such as the construction of Knowledge Graphs, or Question Answering. The need for a NER system able to be trained with few-annotated examples comes in all its urgency in domains where the annotation process requires time, knowledge and expertise (e.g., healthcare, finance, legal), and in low-resource languages. In this survey, starting from a clear definition and description of the few-shot NER (FS-NER) problem, we take stock of the current state-of-the-art and propose a taxonomy which divides algorithms in two macro-categories according to the underlying mechanisms: model-centric and data-centric. For each category, we line-up works as a story to show how the field is moving toward new research directions. Eventually, techniques, limitations, and key aspects are deeply analyzed to facilitate future studies.

Nowadays, really huge volumes of fake news are continuously posted by malicious users with fraudulent goals thus leading to very negative social effects on individuals and society and causing continuous threats to democracy, justice, and public trust. This is particularly relevant in social media platforms (e.g., Facebook, Twitter, Snapchat), due to their intrinsic uncontrolled publishing mechanisms. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies: early detection of fake news is crucial. Unfortunately, the availability of information about news propagation is limited. In this paper, we provided a benchmark framework in order to analyze and discuss the most widely used and promising machine/deep learning techniques for fake news detection, also exploiting different features combinations w.r.t. the ones proposed in the literature. Experiments conducted on well-known and widely used real-world datasets show advantages and drawbacks in terms of accuracy and efficiency for the considered approaches, even in the case of limited content information.

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [10]. In this paper, we present our framework for fake news detection and we discuss in detail an approach based on deep learning that we implemented by using Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

We demonstrate GraphDBLP, a tool to allow researchers for querying the DBLP bibliography as a graph. The DBLP source data were enriched with semantic similarity relationships computed using wordembeddings. A user can interact with the system either via a Web-based GUI or using a shell-interface, both provided with three parametric and pre-defined queries. GraphDBLP would represent a first graph-database instance of the computer scientist network, that can be improved through new relationships and properties on nodes at any time, and this is the main purpose of the tool, that is freely available on Github. To date, GraphDBLP contains 5+ million nodes and 24+ million relationship.

The main objective of this work is the proposal of a decentralized data structure storing a large amount of data under the assumption that it is not possible or convenient to use a single workstation to host all data.The index is distributed over a computer network and the performance of the search, insert, delete operations are close to the traditional indices that use a single workstation.It is based on k-d trees and it is distributed across a network of "peers", where each one hosts a part of the tree and uses message passing for communication between peers.In particular, we propose a novel version of the k-nearest neighbour algorithm that starts the query in a randomly chosen peer and terminates the query as soon as possible.Preliminary experiments have demonstrated that in about 65% of cases it starts a query in a random peer that does not involve the peer containing the root of the tree and in the 98% of cases it terminates the query in a peer that does not contain the root of the tree.

Despite the great amount of research done in the Online Social Networks (OSNs) field, only few works have investigated the use of multimedia data in such realm. Instead, it is the authors' opinion that a novel data model that takes into account the intrinsic characteristics of multimedia may be of great help in managing Multimedia OSNs for providing more effective algorithms. In this paper, we describe a novel OSN data model that supports easy management of multimedia content in a unique framework, providing a more effective and efficient mechanism for data and information management in a variety of applications, especially for Influence Analysis aims.

In this paper, we present a framework supporting information retrieval over corpora of documents using an automatic sematic query expansion approach. The main idea is to expand the set of words used as query terms exploiting the notion of semantic similarity between the concepts related to the search terms. We leverage existing lexical resources and similarity metrics computed among terms to generate - by a proper mapping into a vectorial space - an index for the fast retrieval of a set of terms "semantically correlated" to a given query term. The vector of expanded terms is then exploited in the query stage to retrieve documents that are significantly related to specific combinations of the query terms. Preliminary experimental results concerning efficiency and effectiveness of the proposed approach are reported and discussed.

As well known, Semantic Web technologies make available a set of facilities that allow data to be shared and reused across applications. The last generation of Content Management System (CMS) can leverage such technologies to improve the content management task incorporating semantic annotations of the produced resources. Here, we present the benefits deriving from the application of semantic technologies in a CMS environment. To this goal, we collect preliminary results about the effectiveness of the integration of a semantic annotation engine within the Intrage Web Portal for content management purposes. The obtained results show that the approach is quite promising and encourage the current research.

In this paper we present our development experience of a Semantic Content Management System able to handle and manage uniformly heterogeneous contents of different kinds (texts, video and images) considering the related semantics. To this aim, we exploit several Semantic Web technologies: RDF/OWL for data modeling and representation, SPARQL as querying language, Multimedia Information Extraction techniques, W3C standard models for creating taxonomies and resource annotation, vocabularies and microformats. We also follow the Best Practices and Issues for the Web of Data by reusing LOD information for the Entity annotation of the managed content, thus providing CMS with advanced capabilities such as contents’ authoring and tagging.

In the classical theory of social choice, a set of voters is called to rank a set of alternatives and a social ranking of the alternatives is generated. In this paper, we model recommendation in the context of browsing systems as a social choice problem, where the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We then propose an importance ranking method that strongly resembles the well known PageRank ranking system, and takes into account both the browsing behavior of the users and the intrinsic features of the objects in the collection. We apply the proposed approach in the context of multimedia browsing systems and show that it can generate effective recommendations and can scale well for large data collections.