Conte Corpus: a corpus of political speeches to fight the pandemic in Italy
Authors: Viviana Patti, Komal Florio, Valerio Basile (Università di Torino, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The study of institutional communication related to the pandemic, and to the population's response to it, is of great relevance today. The Italian spokesperson for communication regarding the pandemic has been, during the year 2020, the former Prime Minister Giuseppe Conte. We retrieved 4,860,395 comments from his Facebook official page and built the ConteCorpus, a new Italian resource annotated in CoNLL-U format. A first aim of the research was to evaluate the performance of the model used to annotate the corpus. Models trained on social media texts are usually not very generalizable. Nevertheless, the results of the evaluation were good, especially in parsing metrics, and showed that a parser trained on Twitter data can be successfully applied to Facebook data. A second aim of the research was to provide an overall view of the content of such a large corpus; for this purpose, topic modeling was conducted, training an LDA model. The model generated 5 topics that cover different aspects linked to the pandemic emergency, from economic to political issues. Through the topic modeling we investigated which topics are prevalent on particular days.

Introduction
During the year 2020, the Prime Minister Giuseppe Conte has played a major role in institutional communication, particularly in communication regarding the policies undertaken to manage the health emergency. We assumed that interesting content from the point of view of the response of the population to institutional communications regarding the pandemic would have been found on his social media profiles. Therefore, we created ConteCorpus, retrieving more than 4 million comments from his Facebook page starting from January 2020 until December 2020, and we annotated it in CoNLL-U format. A first aim of the research was to evaluate the performance of the model used to annotate the dataset. Models trained on social media texts usually are poorly generalizable even on text retrieved from the same social media, therefore we wanted to test the performance on Facebook texts of a model trained on Twitter texts. In order to evaluate the model, we created a gold standard by extracting 1,000 sentences from the ConteCorpus and manually revising them. A second aim of the research was to provide an overall view of this large corpus. For this purpose we performed a Topic Modeling. We trained a LDA model sampling 10% of the ConteCorpus. The LDA model generated 5 topics related to different aspects of the pandemic emergency. The model was used to see which topics were the most relevant before and after the announcement of the first and the second period of restrictions adopted to fight the pandemic in Italy. The paper is structured as follows: we first review the relevant literature for our research (section 2), then we describe the data collection and the creation of the corpus (section 3). In section 4, we describe the evaluation we performed of the model we used to annotate the corpus in CoNLL-U format, and in section 5 we report the results of the topic modeling experiment. In section 6 we provide some concluding observations.

Conclusion
As mentioned before, models trained with data from social media are hardly generalizable. This stems from the fact that from a sociolinguistic perspective, the language of social media does not constitute a single variety. So, we expected that the results in the various evaluation metrics we performed would be worse than the results in the evaluation conducted on the PoSTWITA-UD test set. Surprisingly, in some metrics the results on evaluating the ConteCorpus test set were better than the results on the PoSTWITA-UD test set. To offer an overall view of the content of the ConteCorpus we performed topic modeling. The topics generated by the LDA model cover various aspects of the pandemic emergency, with a preponderance of political and economic issues. Unexpectedly, topics identified do not show concern regard the risk of contagion and the possibility of catching the disease.