2022
DOI: 10.3390/ijerph19137720
|View full text |Cite
|
Sign up to set email alerts
|

Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy

Abstract: The pandemic spread rapidly across Italy, putting the region’s health system on the brink of collapse, and generating concern regarding the government’s capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy’s (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Caglia… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
2
1

Year Published

2022
2022
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 33 publications
0
2
1
Order By: Relevance
“…The results of this work indicate a stark presence of fear in the public's response in the COVID-19 communication channel, with an upward trend. Recent studies support that negative sentiments are dominant on social media in the COVID-19 context [48,57] and detect fear and even panic as the prevailing emotion [28,58,59]. Although our findings do not provide grounds for similar conclusions, the steadily increasing presence of fear was detected in users' responses.…”
Section: Discussioncontrasting
confidence: 50%
See 1 more Smart Citation
“…The results of this work indicate a stark presence of fear in the public's response in the COVID-19 communication channel, with an upward trend. Recent studies support that negative sentiments are dominant on social media in the COVID-19 context [48,57] and detect fear and even panic as the prevailing emotion [28,58,59]. Although our findings do not provide grounds for similar conclusions, the steadily increasing presence of fear was detected in users' responses.…”
Section: Discussioncontrasting
confidence: 50%
“…Sentiment analysis was employed to detect the fluctuations of the public's attitude from positive to negative and vice versa using wordbased techniques [20][21][22][23][24]. Other studies conducted a more fine-grained analysis combining sentiment with emotion analysis in order to investigate the dominant emotions and their correlation to external events and various periods [25][26][27][28]. Topic modeling, mainly using the latent Dirichlet allocation algorithm [29], was performed to identify the most debated themes of discussion [23,24,30,31].…”
Section: Introductionmentioning
confidence: 99%
“…Some of these approaches, and their analogs in languages other than English, have been successfully applied to scenarios related to the scope of our research: the effects of the pandemic and massive social events on the general public sentiment of a determined territory. In an effort to measure the emotions expressed by residents of 10 Italian cities during the early months of the pandemic, Fernandez et al performed a lexicon-based sentiment analysis of more than 4 million coronavirus-related tweets [ 36 ], with results pointing at strong emotional responses towards health policies in the country. A similar analysis was also carried out by Kydros et al in Greece during the acute phase of the pandemic [ 37 ].…”
Section: Introductionmentioning
confidence: 99%