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.

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.

A survey from April 2020 showed that during the coronavirus (COVID-19) pandemic, 48 percent of Italian online users looked for local news, while 41 percent of them was more interested in the situation across the country. The current situation in foreign countries was searched by 29 percent of respondents.

Analysis of ‘COVID-19 in Italy’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sudalairajkumar/covid19-in-italy on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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).

Sample of Italian and US news 1/1/2020-20/10/2021obtqained searching: convalescent plasma, hydroxychloroquine, ivermectin, lockdown, mask, vaccine and vitamin D combined in a Boolean AND search with “covid AND (published OR publication OR journal)”. (In Italian,: plasma convalescenti, idrossiclorochina, ivermectina, lockdown, mascherine, vaccino, vitamina D AND “covid AND (ricerca OR pubblicato OR pubblicazione)

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

Italian people perceived the TV newscast as the most reliable source of information regarding the coronavirus (COVID-19), giving a score of 7.3 points. Online news sites and printed media followed in the ranking with 6.8 and 6.6, respectively. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts 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.

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

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

Section 2

Included Data Sources are:

• 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
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**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.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

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 2020 and May 2021 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 text

and ns=(gsars or gout)

Subject is “novel coronaviruses” or “outbreaks and epidemics” and “general news”

and la=X

Language is X (DE, FR, IT, EN)

and rst=tmnb

Restrict to TMNB (major news and business publications)

and wc>300

At least 300 words

and date from 20191001 to 20212005

Date interval

and re=SWITZ

Region 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)

This work is part of the PubliCo research project.

In Italy, the internet provided most news related to coronavirus (COVID-19). Data gathered between February 21 and March 29 indicated that online news dwarfed all other media channels, with a peak of over 421 thousand news releases between March 9 to 15. The volume of news jumped drastically over the first week of enforced lockdown due to the coronavirus outbreak.

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, in the following months the country had to face four new harsh waves of contagion. As of January 1, 2025, 198,638 deaths caused by COVID-19 were reported by the authorities, of which approximately 48.7 thousand in the region of Lombardy, 20.1 thousand in the region of Emilia-Romagna, and roughly 17.6 thousand in Veneto, the regions mostly hit. The total number of cases reported in the country 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 counted about 2.9 million cases. Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of November 2023, 85 percent of the total Italian population was fully vaccinated. For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

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.

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.

In Italy, the TV channel with the highest total stream duration amount was the first channel of the Italian national broadcaster Canale 5, with Over ** million hours from February 23 to March 1, 2025. Second came Rai 1 owned by mass media company Rai. Video display modes As far as video content is concerned, the live mode dwarfed the on-demand one. Over a week at the end of September 2024, the streaming of live videos lasted on average around ** minutes, while the average stream duration of on-demand videos was roughly **** minutes. A similar ratio was registered in the weeks prior. Online video audience The interest for up-to-date news regarding coronavirus (COVID-19) drove the already increasing consumption of online video. News media outlets, especially newspapers’ websites, experienced a significant growth in their online audience over March 2020. The unique users of La7 TV channel, for example, increased from *** million in February 2020 to over **** million as of mid-March 2020.

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.

84 percent of Italian 18-34-year olds believed Facebook to be responsible for spreading false or not accurate information regarding the coronavirus (COVID-19) and its impact. More in detail, the distrust in the social network was lower among older individuals. On the contrary, the radio was perceived as the most reliable medium, with less than 20 percent of respondents believing it was a source of misinformation or fake news about the coronavirus.

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).

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.