Analysis of COVID-related symptoms from a database of Italian tweets

Damiani, Kim Maria (2021) Analysis of COVID-related symptoms from a database of Italian tweets. [Laurea], Università di Bologna, Corso di Studio in Fisica [L-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Creative Commons: Attribuzione - Condividi allo stesso modo 4.0 (CC BY-SA 4.0)

Download (2MB)

Abstract

Twitter is a highly popular social media which on one hand allows information transmission in real time and on the other hand represents a source of open access homogeneous text data. We propose an analysis of the most common self-reported COVID symptoms from a dataset of Italian tweets to investigate the evolution of the pandemic in Italy from the end of September 2020 to the end of January 2021. After manually filtering tweets actually describing COVID symptoms from the database - which contains words related to fever, cough and sore throat - we discuss usefulness of such filtering. We then compare our time series with the daily data of new hospitalisations in Italy, with the aim of building a simple linear regression model that accounts for the delay which is observed from the tweets mentioning individual symptoms to new hospitalisations. We discuss both the results and limitations of linear regression given that our data suggests that the relationship between time series of symptoms tweets and of new hospitalisations changes towards the end of the acquisition.

Abstract
Tipologia del documento
Tesi di laurea (Laurea)
Autore della tesi
Damiani, Kim Maria
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
machine learning,twitter,language processing,COVID
Data di discussione della Tesi
3 Dicembre 2021
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento

^