14 research outputs found
Environmental and climate migrations: an overview of scientific literature using a bibliometric analysis
No full textClimate change is a key issue in twenty-first century international politics, as is evidenced by the twenty-four world conferences devoted to the subject that have been called by the United Nations. The effect that climate change exerts on human migration is a matter that has recently started to acquire considerable importance, not only as an element of public opinion, but also as a subject of academic publications. While available data on the matter is still far from precise and there are no definite figures on the climate change refugees numbers, published research shows that a colossal wave of migration could come about, caused by climate environmental conditions. In the view of the ever-growing importance of the connection between migration, climate and environmental change, the international scientific community has experienced a rapid growth of interest in these matters over the last ten years. The present contribution sets out to present, by means of a bibliometric analysis of scientific publications, a critical ‘snapshot’ of the development of the research that has been undertaken, paying particular attention to the integration of semantic elements that climatic and environmental change have in common with the issue of migration
An Italian lexical resource for incivility detection in online discourses
No full textAbstractThe exponential growth of social media has brought an increasing propagation of online hostile communication and vitriolic discourses, and social media have become a fertile ground for heated discussions that frequently result in the use of insulting and offensive language. Lexical resources containing specific negative words have been widely employed to detect uncivil communication. This paper describes the development and implementation of an innovative resource, namely the Revised HurtLex Lexicon, in which every headword is annotated with an offensiveness level score. The starting point is HurtLex, a multilingual lexicon of hate words. Concentrating on the Italian entries, we revised the terms in HurtLex and derived an offensive score for each lexical item by applying an Item Response Theory model to the ratings provided by a large number of annotators. This resource can be used as part of a lexicon-based approach to track offensive and hateful content. Our work comprises an evaluation of the Revised HurtLex lexicon.</jats:p
How to improve academic well-being: an analysis of the leveraging factors based on the Italian case
No full textAt first glance, for those who start out in it the academic environment may seem attractive,
but they soon experience the difficulties inherent in this type of career. At the same time,
the academic sector is crucial to the social, cultural, and economic development of any
country. Given this important role, it is fundamental for the decision makers to guarantee
the best return on investment made into this sector. The good health of workers has important implications for the quality of their lives since it affects their level of productivity at work, and it is especially relevant for research programmes, where most of the work is
intellectual. In the present research, we have analysed the health of workers without tenure
in the Italian academic environment, i.e. PhD students and short term contract researchers,
in order to understand which factors have the most relevant impact on their state of health. 699 participants (398 females, 301 males) completed an online questionnaire that included both ad hoc Likert-scales and open-ended questions. Our results, elaborated through Structural Equation Modelling and Text Mining techniques, show how researchers experience high levels of anxiety both from the characteristics of the academic environment and from the career advancement system. Specifically, both job-related factors (i.e. perception of fairness, professional growth, and safety perception) and relational factors (i.e. relationships with supervisors and colleagues) predict the anxiety of non-tenured researchers. Furthermore, women researchers show a high level of anxiety compared with male researchers. Policy implications of our findings are provided
Thirty years of research into hate speech: topics of interest and their evolution
No full textThe exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech
Exploring the research dynamics of futures studies: An analysis of six top journals
No full textThis paper focuses on the global literature on Futures Studies and foresight over the last thirty years by using a bibliographic dataset from the Scopus and using an integrated statistical methodological approach. Bibliometric measures, knowledge mapping tools, topic modelling, Geographical Information Systems and network analysis are used to understand the scholarly literature’s evolution, main research areas, temporal evolution, geographical differences, and fragmentation. This allows to outline a separation between research areas and understand the dynamics of the main topics. The aim of this research is to fill the gap in the literature regarding the mapping of research themes in Futures Studies and foresight, as well as their temporal evolution and geographical distribution. Results showed a notable growth in the number of published articles in the last 32 years and identified (through Latent Dirichlet Allocation) 21 topics, which summarize the most important research themes in the context of Future Studies and foresight. A dynamic topic model helped to understand the evolution of topics, while the network analysis provided quantitative measures on the interactions between the topics as well as the international collaborations. Finally, a geographical analysis of both authors and topics highlighted the global distribution of research on Futures Studies
Comparison of two annotation schemes to derive offensiveness scores in HurtLex.
No full textThe recent proliferation of social media has been accompanied by a similar widespread increase in the frequency of hostile online messages and inflammatory
speeches. Social media are nowadays seen as a suitable arena for ill-tempered
debates to be conducted, with the frequent use of insulting terms and other offensive language. Lexical resources containing specific negative words have
been widely employed to detect uncivil communication. This work describes the
development, implementation and comparison of two annotation schemes to derive offensiveness scores in a lexicon of hate words, namely HurtLex. Since determining a given expression’s offensiveness is a highly subjective matter, we
decided to use two different methods to derive offensiveness scores. The first
method consists in a rating scale annotation such that each word has to be assessed on a 5-point Likert scale and to derive a final score we explicitly model
the item response probability trough a unidimensional Graded response Model
(GrM). The second one is the Best-worst scaling method. we analyse whether
these two different methods lead to correlated scores. we propose a qualitativequantitative analysis of the terms that have opposite scores. The two scoring systems are evaluated on two annotated corpora. The introduction of an
offensiveness level score in HurtLex could be helpful as part of a lexicon-based
approach to track offensive and hateful conten
Facebook Debate on Sea Watch 3 Case: Detecting Offensive Language Through Automatic Topic Mining Techniques
No full textOver the years, there has been growing concern about the disproportionate
use of hate speech on social media platforms. In this paper, we
present a text analysis for detecting abusive language in Italian messages on
Facebook, surrounding the debate over the migrant-rescue ship, Sea Watch 3,
and its captain Carola Rackete. The study data consists of more than 130,000
posts retrieved from two pages relating to Matteo Salvini, the leader of the
Italian Lega political party, and from the official Facebook pages of five Italian
newspapers. To explore the presence of offensive and hatred expressions
in the corpus and to establish to what extent social users’ language differs,
depending on the type of Facebook pages analysed, we ran a topic model
based on Latent Dirichlet Allocation. We have complemented this approach
with tools from semantic network analysi
