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During the Covid-19 crisis, many networks have sprung up disseminating information. This study examines the influence of religion during the Covid-19 pandemic. It understands religion as a factor capable of mitigating frustrations and critical situations in society. To this end, a data mining analysis was developed for a set of 107,786 tweets collected from the social platform Twitter in the framework of user-generated content (UGC), linked to the Covid-19 related tweets published by @Pontifex and @Pontifex_es. To achieve this goal, hidden insight data extraction and sentiment analysis are carried out, along with the application of Social Network Analysis (SNA) techniques. The main outcome of the study is the positive correlation between the repercussion of the Pope’s tweets and the evolution of the Covid-19 incidence in Europe. Finally, the Latent Dirichlet Allocation (LDA) algorithm identifies the relevant topics in the analysis.

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The aim of this article is to offer a review of the impact the different methodologies of analysis have on social enterprises, focusing the study on processes that establish a greater universality based on the degree of success achieved in their social objectives, the social reinvestment of their benefits and their democratic organisation, parameters that should favour the creation of a clear and simple method, as well as adaptable to change. The systematics will allow to establish systems for the measurement of the efficiency of social enterprises, in order to both organise objective procedures of comparison and offer support when applying for public aid derived from European and national funds set up for this purpose. The quantification of the social impact of the companies that constitute the social economy is vital to assess and follow up on their social mission.