In May 2020, up to six percent of all online news and posts related to the coronavirus (COVID-19) and released in Italy were false or not accurate. The percentage was calculated on the average volume of posts and articles published by the Italian media outlets, including posts on social media. The peak in the release of fake news was registered in the early stage of the pandemic at the end of January 2020, with 7.3 percent of the coronavirus-related information.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

As of January 1, 2025, the number of active coronavirus (COVID-19) infections in Italy was approximately 218,000. Among these, 42 infected individuals were being treated in intensive care units. Another 1,332 individuals infected with the coronavirus were hospitalized with symptoms, while approximately 217,000 thousand were in isolation at home. The total number of coronavirus cases in Italy reached over 26.9 million (including active cases, individuals who recovered, and individuals who died) as of the same date. The region mostly hit by the spread of the virus was Lombardy, which counted almost 4.4 million cases.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Context

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - WHO

People can catch COVID-19 from others who have the virus. This has been spreading rapidly around the world and Italy is one of the most affected country.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak. - TIME

Content

This dataset is from https://github.com/pcm-dpc/COVID-19 collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale

This dataset has two files

• covid19_italy_province.csv - Province level data of COVID-19 cases
• covid_italy_region.csv - Region level data of COVID-19 cases

Acknowledgements

Data is collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale and is uploaded into this github repo.

Dashboard on the data can be seen here. Picture courtesy is from the dashboard.

Inspiration

Insights on * Spread to various regions over time * Try to predict the spread of COVID-19 ahead of time to take preventive measures

Dataset

This dataset was created by EdoardoPiccolotto

Released under CC0: Public Domain

Contents

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

In Italy, TV newscasts gained trustworthiness during the coronavirus (COVID-19) pandemic. Before the emergency, they were considered a valuable news source by **** percent of Italian people. During the crisis, the percentage rose to **** percent. The same survey from April 2020 revealed that online news outlets of different categories were deemed as less valuable information sources.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

A survey from April 2020 showed that 79 percent of Italian people believed Facebook to be responsible for spreading false or not accurate information regarding the coronavirus (COVID-19) and its impact. Data revealed that television was considered less reliable than Twitter or Instagram.

A survey from April 2020 showed that Italian people considered TV newscast the most reliable news source regarding the coronavirus (COVID-19). The Government followed in the ranking with 48 percent of individuals seeing it as a reliable news source. News shared by friends and family were perceived as more reliable (20 percent) than radio (17 percent).

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

A survey from April 2020 showed that 34 percent of Italian individuals living in the Islands believed newspapers to be responsible for spreading fake or non accurate information about the coronavirus (COVID-19) and its impact. The percentage was lower in the North-West of the country (25 percent).

The COVID-19 pandemic generated (and keeps generating) a huge corpus of news articles, easily retrievable in Factiva with very targeted queries.This dataset, generated with an ad-hoc parser and NLP pipeline, analyzes the frequency of lemmas and named entities in news articles (in German, French, Italian and English ) regarding Switzerland and COVID-19. The analysis of large bodies of grey literature via text mining and computational linguistics is an increasingly frequent approach to understand the large-scale trends of specific topics. We used Factiva, a news monitoring and search engine developed and owned by Dow Jones, to gather and download all the news articles published between January and July 2020 on Covid-19 and Switzerland.Due to Factiva's copyright policy, it is not possible to share the original dataset with the exports of the articles' text; however, we can share the results of our work on the corpus. All the information relevant to reproduce the results is provided.Factiva allows a very granular definition of the queries, and moreover has access to full text articles published by the major media outlet of the world. The query has been defined as follows (syntax in bold, explanation in italics): ((coronavirus or Wuhan virus or corvid19 or corvid 19 or covid19 or covid 19 or ncov or novel coronavirus or sars) and (atleast3 coronavirus or atleast3 wuhan or atleast3 corvid* or atleast3 covid* or atleast3 ncov or atleast3 novel or atleast3 corona*))Keywords for covid19; must appear at least 3 times in the textand ns=(gsars or gout)Subject is “novel coronaviruses” or “outbreaks and epidemics” and “general news”and la=XLanguage is X (DE, FR, IT, EN)and rst=tmnbRestrict to TMNB (major news and business publications)and wc>300At least 300 wordsand date from 20191001 to 20200801Date intervaland re=SWITZRegion is Switzerland It is important to specify some details that characterize the query. The query is not limited to articles published by Swiss media, but to articles regarding Switzerland. The reason is simple: a Swiss user googling for “Schweiz Coronavirus” or for “Coronavirus Ticino” can easily find and read articles published by foreign media outlets (namely, German or Italian) on that topic. If the objective is capturing and describing the information trends to which people are exposed, this approach makes much more sense than limiting the analysis to articles published by Swiss media.Factiva’s field “NS” is a descriptor for the content of the article. “gsars” is defined in Factiva’s documentation as “All news on Severe Acute Respiratory Syndrome”, and “gout” as “The widespread occurrence of an infectious disease affecting many people or animals in a given population at the same time”; however, the way these descriptors are assigned to articles is not specified in the documentation.Finally, the query has been restricted to major news and business publications of at least 300 words. Duplicate check is performed by Factiva. Given the incredibly large amount of articles published on COVID-19, this (absolutely arbitrary) restriction allows retrieving a corpus that is both meaningful and manageable.metadata.xlsx contains information about the articles retrieved (strategy, amount)The PDF files document the execution of the Jupyter notebooks. The zip file contains the lemma and NE frequencies data, divided by language. The "Lemmas" folder contains a CSV file per month and a general timeseries; the "Entities" folder contains a CSV file per month, a general timeseries, plus subsets that are category-specific. For a comprehensive explanation about categories, you can check the PDF files. This work is part of the PubliCo research project.

BackgroundThe COVID-19 pandemic propelled immunology into global news and social media, resulting in the potential for misinterpreting and misusing complex scientific concepts.ObjectiveTo study the extent to which immunology is discussed in news articles and YouTube videos in English and Italian, and if related scientific concepts are used to support specific political or ideological narratives in the context of COVID-19.MethodsIn English and Italian we searched the period 11/09/2019 to 11/09/2022 on YouTube, using the software Mozdeh, for videos mentioning COVID-19 and one of nine immunological concepts: antibody-dependent enhancement, anergy, cytokine storm, herd immunity, hygiene hypothesis, immunity debt, original antigenic sin, oxidative stress and viral interference. We repeated this using MediaCloud for news articles.Four samples of 200 articles/videos were obtained from the randomised data gathered and analysed for mentions of concepts, stance on vaccines, masks, lockdown, social distancing, and political signifiers.ResultsVaccine-negative information was higher in videos than news (8-fold in English, 6-fold in Italian) and higher in Italian than English (4-fold in news, 3-fold in videos). We also observed the existence of information bubbles, where a negative stance towards one intervention was associated with a negative stance to other linked ideas. Some immunological concepts (immunity debt, viral interference, anergy and original antigenic sin) were associated with anti-vaccine or anti-NPI (non-pharmacological intervention) views. Videos in English mentioned politics more frequently than those in Italian and, in all media and languages, politics was more frequently mentioned in anti-guidelines and anti-vaccine media by a factor of 3 in video and of 3–5 in news.ConclusionThere is evidence that some immunological concepts are used to provide credibility to specific narratives and ideological views. The existence of information bubbles supports the concept of the “rabbit hole” effect, where interest in unconventional views/media leads to ever more extreme algorithmic recommendations.

In times of uncertainty, people often seek out information to help alleviate fear, possibly leaving them vulnerable to false information. During the COVID-19 pandemic, we attended to a viral spread of incorrect and misleading information that compromised collective actions and public health measures to contain the spread of the disease. We investigated the influence of fear of COVID-19 on social and cognitive factors including believing in fake news, bullshit receptivity, overclaiming, and problem-solving—within two of the populations that have been severely hit by COVID-19: Italy and the United States of America. To gain a better understanding of the role of misinformation during the early height of the COVID-19 pandemic, we also investigated whether problem-solving ability and socio-cognitive polarization were associated with believing in fake news. Results showed that fear of COVID-19 is related to seeking out information about the virus and avoiding infection in the Italian and American samples, as well as a willingness to share real news (COVID and non-COVID-related) headlines in the American sample. However, fear positively correlated with bullshit receptivity, suggesting that the pandemic might have contributed to creating a situation where people were pushed toward pseudo-profound existential beliefs. Furthermore, problem-solving ability was associated with correctly discerning real or fake news, whereas socio-cognitive polarization was the strongest predictor of believing in fake news in both samples. From these results, we concluded that a construct reflecting cognitive rigidity, neglecting alternative information, and black-and-white thinking negatively predicts the ability to discern fake from real news. Such a construct extends also to reasoning processes based on thinking outside the box and considering alternative information such as problem-solving.

A survey from April 2020 showed that about eight out of ten Italian people believed Facebook to be responsible for spreading false or not accurate information regarding the coronavirus (COVID-19) and its impact. More in detail, 78 percent of male respondents had this opinion, while the percentage amounted to 80 percent among women. However, when it came to information about the pandemic, male respondents seemed to distrust all other news sources more than the female respondents did.

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes
• MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Inspiration

Some useful insi...

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Visual Dashboard (mobile): http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61

Lancet Article: An interactive web-based dashboard to track COVID-19 in real time

Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Data Sources:

World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Additional Information about the Visual Dashboard: https://systems.jhu.edu/research/public-health/ncov/

Contact:

Email: jhusystems@gmail.com

Terms of Use:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of ‘creators’, and a majority playing the role of ‘consumers’. The relative proportion of these groups (approx. 14% creators—86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic. Methods The datasets that we used in this work come from the COVID-19 Infodemics Observatory (https://covid19obs.fbk.eu/#/). Tweets associated with the COVID-19 pandemics (coronavirus, ncov, #Wuhan, covid19, COVID-19, SARSCoV2, COVID) have been automatically collected using the Twitter Filter API. It contains 7.7 million retweets in the case of USA, 300 thousand in the case of Italy and 900 thousand in the case of the UK. The time of the collection goes from the 22nd of January to the 22nd of May for the USA, while for Italy and the UK it goes from the 22nd of January to the 2nd of December. For each tweet we specified the ID code as well as the time at which it was created. In this dataset one can also find the tables necessary to reproduce exactly the figures in the paper.

After the outbreak of the coronavirus (COVID-19) pandemic in Italy as of February 2020, the number of people trying to be the most up to date with the latest news regarding the the emergency the country was facing increased dramatically. Such attitude by the Italian population could been seen in the growth of news websites audience share. Between the 2nd and 8th March 2020, La7 registered the most significant increase in comparison with the previous weeks (255 percent), followed by ANSA (119.1 percent). The website of the all-news channel Rai News ranked third with a growth of 116.7 percent.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

This study examines how Italian national newspapers portrayed COVID-19 vaccines during 2020–2021. A corpus of 5,621 articles from seven newspapers, collected via the Italian National Institute of Health’s daily press review, was analyzed with SketchEngine using corpus-assisted discourse methods. Quantitative analysis showed a rise in vaccine-related coverage at the end of 2020 and throughout 2021. Core terms such as vaccino (vaccine) and vaccinazione (vaccination) were frequent, while occasional use of synonyms like serum and antidote risked creating confusion. Qualitative analysis revealed instances of “false balance,” where anti-vaccine views were presented alongside pro-vaccine perspectives as if equally supported by evidence. These findings suggest that even authoritative outlets reproduced reporting practices that may undermine public understanding of vaccines. Greater awareness of such practices, and closer collaboration between health professionals and communication experts, could help improve the quality of health information in the media.