Text Mining and network analysis to support improvements in legislative action. The case of the earthquake in Emilia-Romagna

Pasquale Pavone, Margherita Russo, Simone Righi e Riccardo Righi
Dipartimento di Economia Marco Biagi-Università di Modena e Reggio Emilia

Pubblicato su Lexicometrica- Actes JADT 2016

Contributo presentato alla Conferenza JADT 2016 – 13ème Journées internationales d’Analyse statistique des Données Textuelles,  7-10 giugno 2016, Nice (France)

Abstract. In the three years after the 2012 earthquake in Emilia-Romagna, through the enactment of more than 350 ordinances, the Commissioner has structured interventions to cope with emergency and reconstruction. The intense law-making, essential to fill a legal vacuum, has enabled to overcome the uncertainties of the difficult phase of recovery. There is agreement among experts and policy makers that a large number of those ordinances was due to the absence of national rules governing the urgent intervention in case of natural disasters. On the push of actions taken in Emilia-Romagna, the Italian Parliament has reopened the debate on a national law on emergency after natural disasters. Through a systematic content analysis of the corpus of ordinances issued in Emilia-Romagna, in this paper we propose a contribution in drafting the decrees related to the law on emergency. Two main strands of analysis have been developed. In the first one, an automatic text analysis, supported by Taltac2, has provided inputs for a factor analysis and a cluster analysis of the thematic areas covered by the ordinances. Four main topics have been singled out: Contribution grant criteria; management of allocation of resources; urgent works for municipalities, schools and churches buildings; interventions to support population. Having associated each ordinance to one of the four topics, a temporal analysis of the issues addressed during the emergency and reconstruction phase highlights the sequence of actions that were undertaken in Emilia. In a second step, the set of terms characterizing each cluster it is used to obtain a redefinition of disjunctive classification towards a fuzzy multi-class. Furthermore, by adopting an hybrid system of text mining, it is possible to extract the legislation (and other ordinances) cited in the ordinances and identify clusters of regulatory areas of reference to meet the emergency and reconstruction following natural disasters. In this second strand, clusters of citations are detected with an algorithm of network analysis (Infomap) based on information theory (Rosvall & Bergstrom, 2008; De Domenico et al, 2015). This analysis highlights subsets of nodes and allows to outline which are the most relevant issues involved in the areas of intervention after a natural disaster.

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