In recent years, Reddit has attracted the interest of many researchers due to its popularity all over the world. In this article, we aim at providing a contribution to the knowledge of this social ...network by investigating three of its aspects, interesting from the scientific viewpoint, and, at the same time, by analysing a large number of applications. In particular, we first propose a definition and an analysis of several stereotypes of both subreddits and authors. This analysis is coupled with the definition of three possible orthogonal taxonomies that help us to classify stereotypes in an appropriate way. Then, we investigate the possible existence of author assortativity in this social medium; specifically, we focus on co-posters, that is, authors who submitted posts on the same subreddit.
The COVID-19 outbreak impacted almost all the aspects of ordinary life. In this context, social networks quickly started playing the role of a sounding board for the content produced by people. ...Studying how dramatic events affect the way people interact with each other and react to poorly known situations is recognized as a relevant research task. Since automatically identifying country-based COVID-19 social posts on generalized social networks, like Twitter and Facebook, is a difficult task, in this work we concentrate on Reddit megathreads, which provide a unique opportunity to study focused reactions of people by both topic and country. We analyze specific reactions and we compare them with a “normal” period, not affected by the pandemic; in particular, we consider structural variations in social posting behavior, emotional reactions under the Plutchik model of basic emotions, and emotional reactions under unconventional emotions, such as skepticism, particularly relevant in the COVID-19 context.
The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a ...network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.
Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to ...provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.
Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart ...Cities, Emergency Management, and Healthcare, despite being a relatively new field of research. The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. We proposed a new benchmark, called EnviroStream, for evaluating SR systems on weather and environmental data. The benchmark includes queries and datasets of different sizes. We adopted I-DLV-sr, a recently released SR system based on Answer Set Programming, as a baseline for query modelling and experimentation. We also showcased continuous online reasoning via a web application.
In the last few years, we have assisted in a great increase of the usage of strings in the most disparate areas. In the meantime, the development of the Internet has brought the necessity of managing ...strings from very different contexts and possibly using different alphabets. This issue is not addressed by the numerous string comparison metrics previously proposed in the literature. In this paper, we aim at providing a contribution in this context. In fact, first we propose an approach to measure the similarity of strings based on different alphabets. Then we show that our approach can be specifically adapted to several classic string comparison metrics and that each specialization can lead to addressing completely different issues.
In this paper, we propose a framework that aims at handling metrics among strings defined over heterogeneous alphabets. Furthermore, we illustrate in detail its application to generalize one of the ...most important string metrics, namely the edit distance. This last activity leads us to define the Multi-Parameterized Edit Distance (MPED). As for this last metric, we investigate its computational properties and solution algorithms, and we present several experiments for its evaluation. As a final contribution, we provide several notes about some possible applications of MPED and other generalized metrics in different scenarios.
The investigation of anomalies is an important element in many scientific research fields. In recent years, this activity has been also extended to social networking and social internetworking, where ...different networks interact with each other. In these research fields, we have recently witnessed an important evolution because, beside networks of people, networks of things are becoming increasingly common. IoT and Multiple IoT scenarios are thus more and more studied. This paper represents a first attempt to investigate anomalies in a Multiple IoT scenario (MIoT). First, we propose a new methodological framework that can make future investigations in this research field easier, coherent, and uniform. Then, in the context of anomaly detection in an MIoT, we define the so-called “forward problem” and “inverse problem”. The definition of these problems allows the investigation of how anomalies depend on inter-node distances, the size of IoT networks, and the degree centrality and closeness centrality of anomalous nodes. The approach proposed herein is applied to a smart city scenario, which is a typical MIoT. Here, data coming from sensors and social networks can boost smart lighting in order to provide citizens with a smart and safe environment.
•A theoretical framework to handle anomalies in multiple IoT scenarios.•Extension of the forward and the inverse problems to anomaly detection in multiple IoT.•Definition of anomaly taxonomies in multiple IoT scenarios.•Formalization of several kinds of anomaly in multiple IoT scenarios.
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