United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms was 12824.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms reached a record high of 26973.00000 in January of 2010 and a record low of 12824.00000 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Breakdown of Revenue by Media Type: Newspapers - Print Newspapers for Newspaper Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on July of 2025.

NeMig are two English and German knowledge graphs constructed from news articles on the topic of migration, collected from online media outlets from Germany and the US, respectively. NeMIg contains rich textual and metadata information, sub-topics and sentiment annotations, as well as named entities extracted from the articles' content and metadata and linked to Wikidata. The graphs are expanded with up to two-hop neighbors from Wikidata of the initial set of linked entities.

NeMig comes in four flavors, for both the German, and the English corpora:

• Base NeMig: contains literals and entities from the corresponding annotated news corpus;
• Entities NeMig: derived from the Base NeMIg by removing all literal nodes, it contains only resource nodes;
• Enriched Entities NeMig: derived from the Entities NeMig by enriching it with up to two-hop neighbors from Wikidata, it contains only resource nodes and Wikidata triples;
• Complete NeMig: the combination of the Base and Enriched Entities NeMig, it contains both literals and resources.

Information about uploaded files:

(all files are b-zipped and in the N-Triples format.)

Graph and download economic data for Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - All Other Advertising for Newspaper Publishers, All Establishments, Employer Firms (RPCNOAEF51111ALLEST) from 2013 to 2021 about periodicals, advertisement, printing, employer firms, accounting, revenue, establishments, services, and USA.

This graph shows the ** most popular daily newspapers in Massachusetts, United States as of December 2016, sorted by circulation. According to the source, The Boston Globe had a daily circulation of ****** thousand as of December 2016.

The graph shows the most popular types of publication among newspaper subscribers in the United States as of March 2017. During a survey, 84 percent of respondents stated that they subscribed to a print version of a newspaper.

The graph presents information on the share of consumers who read daily newspapers in the United States from 1999 to 2014, broken down by ethnic group. The source reports that in 2014, ** percent of Hispanic consumers read any daily newspaper.

The graph presents information on the share of Hispanic consumers who read daily newspapers in the United States from 1999 to 2015. The source reports that in 2014, ** percent of Hispanic consumers read any daily newspaper.

United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - All Other Advertising for Newspaper Publishers, All Establishments, Employer Firms was 8276.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - All Other Advertising for Newspaper Publishers, All Establishments, Employer Firms reached a record high of 15296.00000 in January of 2010 and a record low of 8056.00000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - All Other Advertising for Newspaper Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on June of 2025.

The graph presents information on the percentage of African American consumers who read daily newspapers in the United States from 1999 to 2015. The source reports that in 2014, 28 percent of African American consumers read any daily newspaper.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

Graph and download economic data for Producer Price Index by Industry: Newspaper Publishers: Newspaper Publishing (PCU5131105131101) from Dec 2022 to May 2025 about periodicals, printing, PPI, industry, price index, indexes, price, and USA.

This graph shows the ** most popular daily newspapers in Georgia, United States as of August 2016, sorted by circulation. According to the source, The Augusta Chronicle had a daily circulation of ***** thousand as of August 2016.

Graph and download economic data for Producer Price Index by Industry: Newspaper Publishers: Newspaper Advertising Sales, Print Only (PCU5131105131101111) from Sep 2015 to May 2025 about periodicals, advertisement, printing, sales, PPI, industry, price index, indexes, price, and USA.

Graph and download economic data for Employment for Information: Newspaper Publishers (NAICS 51111) in the United States (IPUJN51111W200000000) from 1987 to 2024 about periodicals, printing, information, NAICS, IP, employment, and USA.

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in https://zenodo.org/records/10911980. The code used for this use case can be found in https://github.com/Gabriel382/DDPF-Health-Risks . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, Aylien (data only from November of 2019) and CORD-19 (data only from December of 2019).

To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command:

docker run --interactive --tty --rm \
--publish=7474:7474 --publish=7687:7687 \
--volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\
neo4j:4.4.3 \
neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force

Graph and download economic data for Producer Price Index by Industry: Newspaper Publishers: Weekly and Other Newspaper Advertising Sales (DISCONTINUED) (PCU5111105111104) from Jun 1999 to Sep 2015 about periodicals, advertisement, printing, sales, PPI, industry, inflation, price index, indexes, price, and USA.

United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - Classified Advertising for Newspaper Publishers, All Establishments, Employer Firms was 1978.00000 Mil. of $ in January of 2021, according to the United States Federal Reserve. Historically, United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - Classified Advertising for Newspaper Publishers, All Establishments, Employer Firms reached a record high of 5082.00000 in January of 2010 and a record low of 1978.00000 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Breakdown of Revenue by Advertising Type: Newspapers Advertising Space - Classified Advertising for Newspaper Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on July of 2025.

The graph presents the most popular newspaper publishers in the United States in December 2013, by number of unique visitors to the publisher's newspaper websites. Mail Online ranked fourth with a total of ***** million unique visitors. The website was visited by ***** million desktop users and ***** million mobile users.

Context

daily updated ERC database for seasonal ERC graphs

Content

averaged ERC value for 14 PSA based on the historical and today's value

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The chart shows that that the oldest Americans, especially those over 65, were more likely to share fake news to their Facebook friends. This is true even when holding other characteristics—including education, ideology, and partisanship—constant. The coefficient on “Age over 65” implies that being in the oldest age group was associated with sharing nearly seven times as many articles from fake news domains on Facebook as those in the youngest age group, or about 2.3 times as many as those in the next-oldest age group, holding the effect of ideology, education, and the total number of web links shared constant.