We asked U.S. consumers about "ePaper / online news website usage by brand" and found that "The New York Times" takes the top spot, while "Sfchronicle.com" is at the other end of the ranking.These results are based on a representative online survey conducted in 2024 among 3,605 consumers in the United States. Looking to gain valuable insights about news websites readers worldwide? Check out our

In 2024, the average weekday print circulation of The New York Times was approximately ******* copies, less than half the figure recorded in 2014. In that year, the company ceased publishing its figures based on weekday circulation for print, online, and other digital platforms, and published only its print circulation. The New York Times The New York Times was founded in 1851 and has been a household name in the United States for decades. The newspaper has adapted well to changes in the media industry, and between the final quarters of 2014 and 2020, paid subscribers to The New York Times’ digital only news product increased from *** thousand to over ************. The New York Times is also one of the world’s leading podcast publishers, with unique streams and downloads of the company’s podcasts reaching tens and sometimes even hundreds of millions per month. Popularity and reliability As one of the most popular news websites in the United States, the NYT has been known to achieve ** million unique monthly visitors, outperforming the likes of NBC News, The Washington Post, and The Guardian. That said, like many news publications, The New York Times has been the subject of controversy over the years. From accusations of liberal bias to its hiring practices, the newspaper has faced challenges regarding not only its published content but also its employees. In spite of this, just ** percent of respondents to a survey seriously doubted the credibility of The New York Times, with most finding the publication to be a reliable source.

Traffic analytics, rankings, and competitive metrics for nytimes.com as of October 2025

nytimes.com is ranked #24 in US with 550.63M Traffic. Categories: Newspapers. Learn more about website traffic, market share, and more!

newyorktimes.com is ranked #94175 in US with 107.19K Traffic. Categories: . Learn more about website traffic, market share, and more!

NYCDOT's Traffic Management Center (TMC) maintains a map of traffic speed detectors throughout the City. The speed detector themselves belong to various city and state agencies. The Traffic Speeds Map is available on the DOT's website. This data feed contains 'real-time' traffic information from locations where NYCDOT picks up sensor feeds within the five boroughs, mostly on major arterials and highways. NYCDOT uses this information for emergency response and management.

Here's the link to the original dataset.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

In April 2025, the news website with the most monthly visits in the United States was nytimes.com, with a total of ***** million monthly visits in that month. In second place was cnn.com with just over *** million visits, followed by foxnews.com with almost a ****** of a million. Online news consumption in the U.S. Americans get their news in a variety of ways, but social media is an increasingly popular option. A survey on social media news consumption revealed that ** percent of Twitter users regularly used the site for news, and Facebook and Reddit were also popular for news among their users. Interestingly though, social media is the least trusted news sources in the United States. News and trust Trust in news sources has become increasingly important to the American news consumer amidst the spread of fake news, and the public are more vocal about whether or not they have faith in a source to report news correctly. Ongoing discussions about the credibility, accuracy and bias of news networks, anchors, TV show hosts, and news media professionals mean that those looking to keep up to date tend to be more cautious than ever before. In general, news audiences are skeptical. In 2020, just **** percent of respondents to a survey investigating the perceived objectivity of the mass media reported having a great deal of trust in the media to report news fully, accurately, and fairly.

Weather Channel had 285.6 million average visitors to its website in the 12 months running to May 2024, making it the leading global news brand worldwide in this respect. Following in second place was the New York Times with 113 million web visitors.

One of the most compelling trends in technology today is the open data and open governance movement. It's not without reason that no less than Tim Berners-Lee himself, the creator of the worldwide web and one of the most preeminent scholars of the Internet, is doing his latest work in getting more government data on the web: in an interview with The New York Times a few years ago he spoke to how even records as mundane as traffic statistics or weather data could drive tinkerers to "make government run better".

New York City has been at the forefront of this movement: mayor Bloomberg formalized a citywide analytics team as the Mayor's Office for Data Analytics in 2013, and the effort has continued under Mayor De Blasio, with the city cementing its first Open Data Plan in July 2015. The resultant NYC Open Data Portal is populated with over 1500 datasets. It was, and is, the largest citywide open data portal in the world.

Nevertheless, a good open data platform is more than a count; it's a function also of all of the maintenance and structure that goes into it. What's a "dataset", who's publishing them, and how well-maintained are they?

This dataset contains the publicly available metadata about the datasets in the NYC Open Data portal, provided in a JSON format.

For an initial exploration of its contents see this blog post.

The graph shows leading daily newspapers with paywalls in the United States from April to September 2014, by number of paid restricted access website accounts. In that time period, Los Angeles Times ranked fourth with nearly ** thousand paid restricted access website accounts. Digital publishing – additional information

The New York Times has been the most successful American daily when it comes to attracting readers willing to pay for its online content. The paywall, which was introduced in March 2011, allows users to read ** articles a month for free. Once that limit has been reached, users are required to pay in order to read more articles. According to the New York Times Company’s own data, the number of paid subscribers to its digital-only products amounted to *** thousand in the second quarter of 2015; a steady growth since its implementation. Circulation revenue now exceeds the advertising revenue. ). The New York Times seems to belong to a successful minority. During a survey in late 2013, more publishers admitted that the introduction of the paywall led to a decrease of traffic on their website. This was the experience of a third of the respondents, whereas a quarter stated the paywall boosted the traffic. Most publishers allow their readers to view **** or *** free articles a month, which makes the aforementioned New York Times model of ** articles more than generous in comparison. In general, the U.S. digital publishing industry is expected to thrive. Between the period 2014 to 2020, revenues are predicted to nearly double, generating approximately **** billion U.S. dollars. Among three major types of digital publishing products – e-books, e-magazines and e-newspapers – it is the latter that will develop the most rapidly over the aforementioned period of time.

Context

Like so many other aggregated metric of human activity, hourly passenger traffic within the NYC subway system was an exceptionally predictable signal, with each station having its regular seasonal, daily and weekly fluctuations - until March of 2020. How can we as data scientists quickly respond to unforeseen events that completely change the nature of the behavior we are trying to model? Should we throw away our old models and data and try to start from scratch, or can we do better?

Content

The main dataset includes the number of subway station entries and exits, as counted by the number of people passing through the turnstiles located at the station entrances, at 4 hour intervals, for 469 subway stations from Feb. 4th 2017 to Aug. 13th 2021.

In addition, a dataset of NYC census data by neighborhood (source: https://furmancenter.org/neighborhoods) is provided as an auxiliary dataset. Each of the 469 stations in the main dataset was referenced to one of 51 neighborhoods, each associated with 87 aggregate financial and demographic variables.

Preprocessing

See the accompanying notebook for the full data acquisition and preprocessing script. You may use it to generate a new updated dataset with up-to-date traffic data.

The data was downloaded from the MTA website (http://web.mta.info/developers/turnstile.html), where is it available as weekly data per turnstile but suffers from noisy samples (e.g. from bad turnstile counters), missing data and a confusing hierarchical structure of subway station elements (turnstile machine, control area, remote unit, station, etc.).

To make the data usable, the following preprocessing steps were taken: 1. For each turnstile's data, resample the data to fixed 4-hour intervals (instead of having some samples referenced to 2pm and others to 3pm, etc.). The given timestamp in each sample corresponds to the center of this interval (e.g. 4pm corresponds to the 2-6pm interval). 2. Convert the cumulative sum of entries/exits reported by each turnstile to an absolute number of passengers for that 4-hour interval 3. Drop missing data, outliers and and negative values, and drop stations with bad data or too many missing data points over time 4. Aggregate over turnstiles belonging to the same station (assuming we don't care how many people pass through each individual turnstile) 5. Join the time series data with station metadata such as latitude and longitude, daytimes routes, station structure etc. Since some stations have multiple rows for multiple connecting lines, these single-character lines are concatenated for the stations, for example "LNQR456" indicates 7 separate connecting lines. Also, some stations have the same name, e.g. "103 st. which corresponds to three actual stations along this street both in the Upper West Side and East Harlem. 6. Generate a new "Unique ID" that corresponds to a unique combination of Station and Line (with Remote Unit, Connecting Lines, Daytime Routes, and North/South Direction Label being also unique as result). This identifier, not included in the original data, is the most suitable hierarchical level for modeling aggregated passenger traffic through the stations, as it corresponds to a specific line within a specific stop, but aggregated over individual turnstiles. 7. Add a neighborhood ID for each station, based on the lat-long coordinates and a neighborhood shapefile (downloaded from https://geodata.lib.berkeley.edu). Neighborhood names in the NYC census dataset were manually edited to match these neighborhood names.

Inspiration

This dataset is a great example for time series data that was drastically affected by the COVID19 outbreak, with subway passenger traffic plummeting during March of 2020 and very slowly climbing back since. It can be used for any kind of task that requires time series / geospatial data, and in particular analyses interested in investigating concept drift or "New Normal" scenarios.

Acknowledgements

I spent a lot of effort generating this dataset (for an internal research project) because none of the existing resources suited my needs, but several versions of this data can be found on Kaggle and elsewhere. See for example: https://www.kaggle.com/new-york-state/nys-turnstile-usage-data https://www.kaggle.com/cyaris/mta-turnstile-traffic https://www.kaggle.com/monsieurwagner/nyctransit https://medium.com/qri-io/taming-the-mtas-unruly-turnstile-data-c945f5f96ba0