Abstract: People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research uses a treemap visualization to identify different users profile on Twitter. For this purpose, we exploit users' profile and tweeting behavior information. We evaluate our approach by visualizing the different Twitter profiles. This treemap visualization technique can be used to identify easily the different users’ profile in a wide range of users. We focus just on user activity, ignoring the content of messages. We take into consideration both social interactions and tweeting patterns, which allow us to profile users according to their activity patterns using treemaps.

Abstract: The construction of social network graphs from online networks data has become nowadays a common track to analyze these data. Typical research questions in this domain are related to profile building, interest’s recommendation, and trending topics prediction. However, few work has been devoted to the analysis of the evolution of very short and unpredictable events, called polemics. Also, experts do not use tools coming from social network graphs analysis and classical graph theory for this analysis. In this way, this article shows that such analysis lead to a colossal amount of data collected from public social sources like Twitter. The main problem is collecting enough evidences about a non-predictable event, which requires capturing a complete history before and during the course of this event, and processing them. To cope with this problem, while waiting for an event, we captured social data without filtering it, which required more than a TB of disk space. Then, we conduct a time-related social network analysis. The first one is dedicated to the study of the evolution of the actor interactions, using time-series built from a total of 33 graph theory metrics. A Big Data pipeline allows us to validate these techniques on a complex dataset of 284 millions of tweets, analyzing 56 days of the Volkswagen scandal [12].

Several new microdeletion and microduplication syndromes are emerging as disorders that have been proven to cause multisystem pathologies frequently associated with intellectual disability (ID), multiple congenital anomalies (MCA), autistic spectrum disorders (ASD) and other phenotypic findings. In this paper, we review the "new" and emergent microdeletion and microduplication syndromes that have been described and recognized in recent years with the aim of summarizing their main characteristics and chromosomal regions involved. We decided to group them by genomic region and within these groupings have classified them into those that include ID, MCA, ASD or other findings. This review does not intend to be exhaustive but is rather a quick guide to help pediatricians, clinical geneticists, cytogeneticists and/or molecular geneticists.