Luca Pareschi

PERCEIVE deliverable 5.3 ‘Production of a report discussing (including visualizingtopographic maps of meanings) the emergent topics in identityrelevant discourse at the different levels’

In this report we describe the results of the analysis that we performed through topic modeling on the texts that we collected and described in the previous deliverable 5.2. In particular, our aim was to analyze the latent meaning structure, and the shared meanings of EU policies and EU identity, on four levels of communication: 1) Communications of the EU: here we analyzed the magazine Panorama; 2) Local implementation: here we analyzed financed projects’ abstracts; 3) Local press: at this level we focus on newspapers; 4) Social media: at this level we focus on Facebook. The analysis performed with Topic Modeling on the corpus of tweets collected from Twitter did not provide good results, due to the short length of each tweet. It is a result that is expected, but collected data are not useless. In order to provide a better coherence of this report, which focuses on the results of Topic Modeling, we are therefore not providing an analysis of tweets here. On the contrary, we will analyze tweet in the next deliverable 3.3, that is aimed at analyzing the use LMAs make of social media. With regards to the other level, we are presenting 16 models: one model for Panorama, one model for financed projects’ abstracts, 7 models for newspapers – one for each country – and 7 models for Facebook. Regarding Facebook, LMAs in our case study region in UK do not have a Facebook profile. The seventh model regards thus European Institutions’ Facebook profiles. Each model is composed of a list of 20 topics, which we analyze and characterize through descriptive statististics. We focus in particular on the emergent topics related to European Identity and Cohesion Policy. Moreover, in this report, we make use of formal methods and techniques to visually represent the meaning of topics elicited through topic modeling. The following paragraph provides a short introduction to the technique of topic modeling. Then four paragraph accounts for the several topic models elicited at different level of communication. Finally, conclusions make sense of the whole analysis.

No country for neoliberalism: a topic modeling approach to protean discourses to resist privatizations in Italy

This paper analyzes the resistance to the neoliberal discourse supporting privatizations in the Italian sociopolitical field: we address the change from a state control over economy to a situation where most of the state owned enterprises are sold and neoliberal principles are widely adopted and accepted. We focus on resistance, which builds on two frames that differ according to the period when they arise, the words they are composed of, the meanings they bear upon. The first one, which is more prevalent in the period 1984-2000, and that we called Ovalues of developmental stateO, opposes privatizations from a technical point of view: it is used in quotes that rationally support state intervention in economy. The second frame, that we called Ostigma privatizationsO, becomes prevalent starting in 2000 and appears mainly in articles that deal with societal issues, literature, movies and the wider sociocultural debate. Here influential speakers blame privatizations as something that eroded societal cohesion. To explain the transformation, we mobilize the concept of capital as described by Bourdieu: as economic capital attached to delegitimized institutions erode, discourse on resistance does not disappear but is framed within the fields that are less dependent on economic capital. As a connected contribution, the key role played by cultural capital in preserving areas of resistances revives the debate on the role of intellectuals within power dynamics as described by Antonio Gramsci. From a technical point of view, we study the evolution of the vocabulary of privatizations by analyzing almost 70.000 articles in the period 1984-2014. we use Topic Modeling, that is an automated text analysis technique that elicits topics, which are the sets of words that constitute discourses. We then reconstruct frames starting from these topics.