Because of the widespread diffusion of semistructured data in XML format, much research effort is currently devoted to support the storage and retrieval of large collections of such documents. XML documents can be compared as to their structural similarity, in order to group them into clusters so that different storage, retrieval, and processing techniques can be effectively exploited. In this scenario, an efficient and effective similarity function is the key of a successful data management process. We present an approach for detecting structural similarity between XML documents which significantly differs from standard methods based on graph-matching algorithms, and allows a significant reduction of the required computation costs. Our proposal roughly consists of linearizing the structure of each XML document, by representing it as a numerical sequence and, then, comparing such sequences through the analysis of their frequencies. First, some basic strategies for encoding a document are proposed, which can focus on diverse structural facets. Moreover, the theory of discrete Fourier transform is exploited to effectively and efficiently compare the encoded documents (i.e., signals) in the domain of frequencies. Experimental results reveal the effectiveness of the approach, also in comparison with standard methods.

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.

With the rise of social media, individuals face challenges in decision-making due to the abundance of options available. Recommender Systems (RSs) leverage Artificial Intelligence (AI) to provide users with personalized suggestions aligned with their preferences and interests. This study presents a systematic review of AI-based Recommender Systems, focusing on recent advancements and primary studies published between 2019 and 2024. While several review papers have addressed various aspects of RSs, the rapid evolution of AI techniques necessitates an updated review to capture the latest trends and innovations. We systematically gathered data from five major databases: IEEE, Springer, Science Direct, ACM, and Wiley. Through the PRISMA methodology, we selected 85 relevant studies. Our analysis addresses several key research questions: the types of datasets and data sources used, major application fields, prevalent machine learning and AI techniques, overall research productivity, and the limitations and future trends in AI-based RSs. Our findings indicate that advanced AI techniques, particularly those incorporating deep learning with multiple hidden layers and transformer models like BERT, significantly enhance the accuracy and effectiveness of Recommender Systems. Furthermore, we observed a trend towards integrating contextual and real-time data to improve recommendation relevance. Additionally, we discuss ethical considerations such as privacy, data security, bias, and transparency, emphasizing the need for responsible AI development to ensure fair and equitable recommendations. These insights can guide future research and development efforts in the field.

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).