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Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution. [ABSTRACT FROM AUTHOR]

A bibliographic record, related to a product, is composed by different information: authors, year, source, publisher, keywords, abstract, citations and so on. Citations usually have a central role in bibliometric analysis. The study of textual information could be a different analysis perspective. The idea is that documents are mixture of latent topics, where a topic is a probability distribution over words. In this paper we try to show how the scientific productivity of a research group can be described using topic models. Moreover, for the same sample, we test if the other bibliometric measures follow the known distribution laws. [ABSTRACT FROM AUTHOR]

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On 18th February the first Italian case of Coronavirus Induced Disease 2019 (COVID19) due to secondary transmission outside China was identified in Codogno, Lombardia region. In the following days the number of cases started to rise not only in Lombardia but also in other Italian regions, although Lombardia remained and it is still the most affected region in Italy. At the moment, 234801 cases have been identified in Italy, out of which 90070 in Lombardia region. The (Severe Acute Respiratory Syndrome Coronavirus 2) SARS CoV 2 outbreak in Italy has been characterized by a massive spread of news coming from both official and unofficial sources leading what has been defined as infodemia, an over-abundance of information - some accurate and some not - that has made hard for people to find trustworthy sources and reliable guidance needed. Infodemia on SARS CoV 2 created the perfect field to build uncertainty in the population, which was scared and not prepared to face this outbreak. It is understandable how the rapid increase of the cases' number , the massive spread of news and the adoption of laws to face this outbreak led to a feeling of anxiety in the population whose everyday life changed very quickly. A way to assess the dynamic burden of social anxiety is a context analysis of major social networks activities over the Internet. To this aim Twitter represents a possible ideal tool since the focused role of the tweets according to the more urgent needs of information and communication rather than general aspects of social projection and debate as in the case of Facebook, which could provide slower responses for the fast individual and social context evolution dynamics.  Aim of the paper is to analyse the most common reasons for calling and outcomes. Furthermore, the joint analysis with Twitter trends related to emergency services might be useful to understand possible correlations with epidemic trends and predict new outbreaks.

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started in December 2019 in China and was declared a pandemic on 11.03.2020 by WHO. Italy is one of the most afflicted Country by this epidemic with 136,110 confirmed cases and 16,654 deaths on 9.4.2020 (at the same date, the Ministry of Health was reporting 143,626 cases).  During these few months the National Health Service have made a great effort to cope with the increasing request of intensive care beds and all the elective activities in hospital have been suspended. Data from the different Italian regions shows different patterns of positive and dead for this syndrome. Moreover, striking differences of the observed lethality of the infections among different areas were immediately evident from the epidemic reports. It will be of critical relevance to understand the expected evolution of the first lock-down phase, driving the exhaustion of the Covid-19 outbreak.(www.actabiomedica.it).