@inproceedings{pernisi-etal-2024-monica,
title = "{MONICA}: Monitoring Coverage and Attitudes of {I}talian Measures in Response to {COVID}-19",
author = "Pernisi, Fabio and
Attanasio, Giuseppe and
Nozza, Debora",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.83/",
pages = "766--773",
ISBN = "979-12-210-7060-6",
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 will release the data at URL."
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%0 Conference Proceedings
%T MONICA: Monitoring Coverage and Attitudes of Italian Measures in Response to COVID-19
%A Pernisi, Fabio
%A Attanasio, Giuseppe
%A Nozza, Debora
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F pernisi-etal-2024-monica
%X 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 will release the data at URL.
%U https://aclanthology.org/2024.clicit-1.83/
%P 766-773
Markdown (Informal)
[MONICA: Monitoring Coverage and Attitudes of Italian Measures in Response to COVID-19](https://aclanthology.org/2024.clicit-1.83/) (Pernisi et al., CLiC-it 2024)
ACL