MONICA: Monitoring Coverage and Attitudes of Italian Measures in Response to COVID-19
Authors: Fabio Pernisi, Giuseppe Attanasio, Debora Nozza (Università Bocconi, Instituto de Telecomunicações, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Modern social media have long been observed as a mirror for public discourse and opinions. Especially in the face of exceptional events, computational language tools are valuable for understanding public sentiment and reacting quickly. During the coronavirus pandemic, the Italian government issued a series of financial measures, each unique in target, requirements, and benefits. Despite the widespread dissemination of these measures, it is currently unclear how they were perceived and whether they ultimately achieved their goal. In this paper, we document the collection and release of \monica, a new social media dataset for MONItoring Coverage and Attitudes to such measures. Data include approximately ten thousand posts discussing a variety of measures in ten months. We collected annotations for sentiment, emotion, irony, and topics for each post. We conducted an extensive analysis using computational models to learn these aspects from text. We release a compliant version of the dataset to foster future research on computational approaches for understanding public opinion about government measures. We release data and code at .

Introduction
Understanding public opinion on governmental decisions has always been crucial for assessing policies' effectiveness, especially when facing exceptional events requiring prompt decisions. Computational linguistics and social scientists have long observed modern social media platforms as they are a perfect stage for spreading opinions swiftly and transparently. Natural Language Processing (NLP) techniques have been widely used for analyzing public discussion \citep[e.g.,][]{medhat2014sentiment,giachanou2016like,qian2022understanding}. The COVID-19 pandemic, arguably the most prominent of such exceptional events, prompted the Italian government---and other European governments---to release multiple financial measures to cushion the impact on the population. These so-called ``bonuses,'' issued pro bono, i.e., with no interest payments from recipients, aimed at increasing liquidity and reducing tax burdens. However, despite reaching varied recipients, comprehending the measures' reception and evaluating their effectiveness still needs to be explored. To address this gap, we collect and release \monica, a new social media dataset for MONItoring Coverage and Attitudes of Italian measures to COVID-19. \monica comprises approximately 10,000 posts spanning ten months collected on X.com. These posts pertain to the Italian public's discussions on diverse financial measures introduced during the pandemic. Building on an extensive body of literature that examines public sentiment during the pandemic \citep[e.g.,][]{muller2023covid,chen2020tracking,kaur2020monitoring,scott2021measuring,wang2020covid}, this work offers new insights into the limited research specifically addressing Italy. for one of the early (and few) works on modelling sentiment from Twitter during the COVID-19 outbreak. This paper details the dataset's collection and release. It introduces the annotations we compiled for each post, including sentiment, emotion, irony, and discussion topics. Then, we conducted an analysis using traditional models and transformer-based language models to predict these aspects from textual data, demonstrating the dataset's potential usability. Moreover, using state-of-the-art interpretability tools, we explained the models' decision processes. We found that explanations are faithful and plausible to human judgments. \monica will allow a retrospective examination of the efficacy -- and inefficacy -- of governmental measures implemented in Italy during the COVID-19 pandemic, as perceived by the population. By doing so, we seek to provide insights that can inform policymakers about the strengths and weaknesses of such financial measures, ensuring better preparedness and response strategies for any future crises. \paragraph{Contributions.} We release \monica, a GDPR-compliant dataset of social media posts to monitor the coverage and people's attitude towards Italy's government's financial aid to combat the COVID-19 crisis. We collect annotations of several aspects to allow for a finer-grained analysis. We used state-of-the-art NLP and interpretability tools and reported key insights on public sentiment.

Conclusion
We documented the collection and release of \monica, the first large-scale dataset for monitoring the coverage and attitudes of financial aid enacted by the Italian government during the COVID-19 pandemic. It counts around 10,000 annotated posts for subjectivity, sentiment, emotion, irony, and topic. We conducted a first analysis and discovered that (1) most posts have a negative tone and (2) NLP and machine learning models can help detect it. Finally, we conducted a preliminary explainability study to understand how models predict sentiment from text. We found that explanation quality varies across methods and recommended LIME as a sensible starting choice. Our dataset and study fill a critical research gap by examining Italian public sentiment towards COVID-19 measures. Future research will build on this groundwork to build more effective opinion monitoring and mining tools and ultimately inform prompt and targeted policy decisions. Additionally, to better understand the severity of negative attitude, future research may concentrate on examining hate speech in relation to public policies during the pandemic in Italy AUTHOR.