In this paper, we present a Social Network Analysis based approach to investigate user behavior during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behavior of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneauvered behind the Ethereum bubble analyzed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.

In this paper, we propose an investigation of negative reviews and define the profile of negative influencers in Yelp. The methodology adopted to achieve this goal consists of two phases. The first one is theoretical and aims at defining a multi-dimensional social network based model of Yelp, three stereotypes of Yelp users, and a network based model to represent negative reviewers and their relationships. The second phase is experimental and consists in the definition of five hypotheses on negative reviews and reviewers in Yelp and their verification through an extensive data analysis campaign. This was performed on Yelp data represented by means of the models introduced during the first phase. Its most important result is the construction of the profile of negative influencers in Yelp. The main novelties of this paper are: (i) the definition of the two social network based models of Yelp and its users; (ii) the definition of three stereotypes of Yelp users and their characteristics; (iii) the construction of the profile of negative influencers in Yelp.

In recent years, Reddit has attracted the interest of many researchers due to its popularity all over the world. In this paper, 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 analyzing 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, i.e., authors who submitted posts on the same subreddit.

Slips, trips and falls are among the main causes of accidents in a workplace. For this reason, many fall detection approaches have been proposed in the literature. One of the most important categories of approaches is based on the usage of wearable devices. These devices have many advantages, but they also pose some challenging open issues. In particular, they must not be bulky, must have low power consumption and must be able to optimize the low computational power available. In this paper, we aim at facing these challenges by proposing SaveMeNow.AI, a new wearable device for fall detection. SaveMeNow.AI is based on the deployment of a Machine Learning approach for fall detection embedded in it. This approach exploits data continuously measured by a six-axis IMU present inside the device.