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 ).

Financial Natural Language Processing crucially leverages social media for market insights. However, most existing methods for this purpose rely on simple sentiment analysis models, which fail to capture the concrete trading intentions expressed in these discussions. While Large Language Models (LLMs) offer a promising alternative to simplistic sentiment analysis, the actual benefits of fine-tuning across different model families remain unclear in noisy, domain-specific contexts like online forums. To address this gap, we present a comprehensive assessment of the advantages and limitations of fine-tuning for Social Trading Action Detection (STAD), a novel task that aims to classify online posts into actionable categories, namely buy, sell, or other. In addition, we introduce FinReddit-2K, a manually annotated dataset consisting of 2123 Reddit posts, designed to serve as a benchmark for this task. Our experimental analysis goes beyond standard performance metrics and identifies both the types of errors that fine-tuning can successfully mitigate and those that it may inadvertently introduce. Through a systematic evaluation of 57 models, comparing 14 traditional models with 23 zero-shot LLMs and 20 fine-tuned variants, our results show that fine-tuning yields an average F1-score improvement of ＋15.1%. The best-performing model, a fine-tuned Mistral-7B, achieves an F1-score of 86.0%, although our analysis reveals that fine-tuning fails to produce meaningful performance gains in several scenarios.

Synthetic Data Generation (SDG) is expanding rapidly, yet existing surveys differ widely in scope and methodological quality. This tertiary study systematically searched four major scholarly databases (2015-2025) and, after PRISMA screening and DARE-4 appraisal, 1 identified 17 eligible secondary studies. The evidence reveals a strong concentration in healthcare (58.8% of surveys), limited coverage of non-health domains, and inconsistent reporting of evaluation protocols (e.g., incomplete specification of metrics, data splits, baselines, or evaluation scripts). Fidelity and downstream utility dominate assessment practices, whereas privacy and diversity remain under-examined. Only 4 of 17 surveys provide any reproducibility artefacts. By consolidating these findings, we propose a compact, domain-agnostic evaluation baseline and highlight structural gaps in transparency, domain breadth, and methodological consistency. The study offers actionable guidance for strengthening reproducibility and broadening the evidential foundations of SDG research.

In this paper, we present a stock market forecasting framework that integrates sentiment analysis of news headlines associated with individual stocks performed by Large Language Models (LLMs) with economic indicators. Specifically, we infer multi-class labels and continuous polarity scores by using LLMs (i.e., Llama, Vicuna, and Mistral) under zero-shot settings from news content. These sentiment signals are combined with historical price data and economic indicators and fed as input to different deep learning models (i.e., Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and Transformers) for predicting stock closing prices. We evaluate the proposed framework on a dataset comprising historical financial time series and 8652 news articles related to 47 meme stocks listed on major stock exchanges, covering the period from January 2019 to December 2021. While Vicuna delivers the quickest sentiment processing (around faster than Llama), it achieves the greatest hallucination rate ( ). Among the forecasting models, Transformer architectures offer improved Mean Absolute Percentage Error (MAPE) scores ( – ) while LSTM requires minimal computational resources for training (up to ).

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.

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 pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V's).

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.

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.