The 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.

The paper is an investigation on the impact of financial markets on the volatility of the green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) before and during the pandemic period. To this purpose, after observing the dynamic joint correlations between all the variables, we adopt Exponential and Generalized AutoRegressive Conditional Heteroskedasticity models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two Exponential models is positive: so, the “green” credit risk volatility is more sensitive to positive shocks than to negative ones. With reference to the conditional mean, we note that if during the non-pandemic period only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of the green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context and interesting in terms of policy implications.

Purpose: This work aims at discovering the multifaceted business performance of SA8000-certified companies operating in the Italian food and beverages manufacturing industry.Design/methodology/approach: A clustering based on a principal component analysis considering 20 profitability, productivity and value-added distribution ratios was carried out on a sample of 105 Italian SA8000-certified food and beverages manufacturing companies.Findings: Two clusters (CL1 and CL2) emerged from the statistical analysis, where CL1 represents 85.71% of the sample. Despite their general and some labour-related features being similar, they show very different performances. While CL2 performs better both within the food and beverage manufacturing industry than CL1, the latter performs similarly to industry, although even lower. Labour productivity pushes the gap between the clusters mostly through sales (generating revenues) and production (in the aspect of generating personnel costs). These findings suggest that a nonunique performance profile of firms SA8000 certified exists and that low performances do not prevent certification.Originality/value: By focusing on SA8000 standard in the food and beverage sector, the study contributes to the research field by investigating the relationships between corporate social responsibility and firm performance in an important industrial sector for the Italian economy.