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The exponential growth of connected devices (i.e. laptops, smartphones or tablets) has radically changed communications means, also making it faster and impersonal by using On-line Social Networks and Instant messaging through several apps. In this paper we discuss about the cyberbullying problem, focusing on the analysis of the state-of-the-art approaches that can be classified in four different tasks (Binary Classification, Role Identification, Severity Score Computation and Incident prediction). In particular, the first task aims to predict if a particular action is aggressive or not based on the analysis of different features. In turn, the second and the third task investigate the cyberbullying problem by identifying users’ role in the exchanged message or assigning a severity score to a given users or session respectively. Nevertheless, information heterogeneity, due to different multimedia contents (i.e. text, emojis, stickers or gifs), and the use of datasets, which are typically unlabeled or manually labelled, create continuous challenges in addressing the cyberbullying problem.

Researchers and companies are making great efforts to create new ways of interaction with a increasing number and types of electronic devices, with particular attention to the conversational interface, whether spoken or written. The cultural heritage domain can bring many benefits from the great effort in this field, as a smart conversational system can provide both room/hotel/apartment information and information related to the area, natural or cultural points of interest, tourist routes, social events, history etc. The paper’s main goal is to present a quick and effective service to the tourists visiting a city for the first time. Learning the cultural preferences of the users can enhance customer satisfaction, since a smart system can propose them customised tours in order to reach point of interests according to the expressed preferences. The interaction with the user is simplified by a conversational interface (chatbot). © Springer Nature Switzerland AG 2021.

Failure rates of Hard Disk Drives (HDDs) are high and often due to a variety of different conditions. Thus, there is increasing demand for technologies dedicated to anticipating possible causes of failure, so to allow for preventive maintenance operations. In this paper, we propose a framework to predict HDD health status according to a long short-term memory (LSTM) model. We also employ eXplainable Artificial Intelligence (XAI) tools, to provide effective explanations of the model decisions, thus making the final results more useful to human decision-making processes. We extensively evaluate our approach on standard data-sets, proving its feasibility for real world applications.

Online social network (OSN) is quickly becoming a promising field of interest for big data analytics and for a number of real applications, ranging from marketing to user profiling and recommendation. Anyway, although OSNs are naturally formed by heterogeneous data, in the recent past, only a few works have considered an explicit use of multimedia in their models. In this paper, we explicitly take into account the intrinsic characteristics of multimedia, having the awareness that in this way, both the models and the analysis algorithms will enormously benefit from such kind of information. In particular, we describe a multimedia data model for OSN, in order to provide, in a unique framework, novel mechanisms for effective management of multimedia information supporting several classic applications, such as influence analysis and maximization. Experiments have been carried out on Flickr Creative Commons Y FCC100M dataset containing about 100 million images, showing that the proposed approach combines both time and efficacy performances.

Social networks analysis is an emerging trend among scholars and researchers in the last years. A great number of companies are interested in social networks data mining. Data gathered from Facebook, Twitter or other social networks result to be very attractive in many application fields, like economics analysis, sentiment analysis, and politics analysis and so on. In our work, we focused on the analysis of the content of textual information obtained from the social media. Our investigation is finalized to extract hot topics in social network. We considered, as case study, reviews obtained from the social network Yelp.

In this paper, inspired by hypergraph-based approaches, we propose a novel data model for social media networks: it allows to represent in a simple way all the different kinds of relationships that are typical of these environments (among multimedia contents, among users and multimedia content and among users themselves) and to enable several kinds of analytics and applications. From the other hand, we have tested several influence maximization algorithms leveraging the introduced network structure in order to show the advantages to consider also “user-to-multimedia” relationships (in addition to the “user-to-user” ones) in the influence analysis problem. Preliminary experiments using data of several social media networks shows how our approach obtains very promising results.

Social media provide a great amount of valuable information in the form of messages posted by users. Information extracted from posts can be considered like features giving insights about the preferences of users towards certain events. These features can be used to generate recommendations looking forward for upcoming events they might find interesting. In this work we present system for opinion analysis from tweets and recommendation of cultural heritage events. At this aim, we detect the events of interest from Tweets and propose a methodology for associating a sentiment degree with a tweet using NLP techniques.

This paper proposes a new distributed data structure based on binary trees to support k-nearest neighbor queries over very large databases. The indexing structure is distributed across a network of “peers”, where each one hosts a part of the tree and communication among nodes is realized by message passing. The advantages of this kind of approach are mainly two: it is possible to (i) handle a larger number of nodes and points than a single peer based architecture and (ii) to manage in an efficient way computation of multiple queries. In particular, we propose a novel version of the k-nearest neighbor algorithm that is able to start the query in a randomly chosen peer. Preliminary experiments have demonstrated that in about 65 % of cases a query, which starts in random node, does not involve the peer containing the root of the tree.