The Times had an average monthly reach of around 15 million adults in the United Kingdom from April 2019 to March 2020. The print and digital reach of The Times and The Times on Sunday was higher among women than men, with over 7.7 million women a month reached by The Times or its website on average. Reach was also significantly higher amongst older consumers, with 11.4 million in the over 35 years age group accessing The Times each month.

Print outdoes online

The Times readership demographics reveal that more women read the newspaper than men, and that 4.1 million households with children get the daily paper every month. Of the leading national newspapers in the United Kingdom in 2019, The Times was the only one to have a higher reach in print than any online version.

Times Newspapers

The Sunday Times and The Times are both owned by Times Newspapers Ltd and based in London. The Sunday Times is published once a week and is the second most successful Sunday newspaper in the UK. The Times is published daily from Monday to Saturday.

As of June 2024, The Times newspaper had 107,000 print subscribers. The number of digital subscribers on the other hand was over five times higher. However, The Times had the lowest average issue print readership among all News UK publications, and under one million.

This statistic illustrates the share of readers of the New York Times in the past 2 weeks in the United States. As of September 2024, ** percent of 18 - 29 year old consumers do so in the U.S. This is according to exclusive results from the Consumer Insights Global survey which shows that ** percent of 30 - 49 year old customers also fall into this category.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

As of June 2024, the News UK paper with the highest average print readership was The Sun (Mon-Sat), with over 1.9 million people having read or looked at an average issue of the publication. The Times had an average readership of under one million in the same time period.

This paper compares the volume of news articles per section in newspapers and social media platforms. To this end, two weeks of news articles were retrieved by querying the public Application Programming Interfaces (APIs) of The New York Times and The Guardian and the diffusion of each article on social media platforms Twitter, Facebook, Google+, Delicious, Pinterest, and StumbleUpon, was tracked. The results show significant differences in the topics emphasized by newspaper editors and social media users. While users of social media platforms favor opinion pieces, along with national, local, and world news, in sharp contrast the decision of news editors emphasized sports and the economy, but also entertainment and celebrity news. Common to social networking sites is the prevalence of items about arts, technology, and opinion pieces. Niche social networks like StumbleUpon and Delicious presented a greater volume of articles about science and technology, while Pinterest is mostly dedicated to fashion, arts, lifestyle, and entertainment. Twitter is the only social network to have presented a statistically significant correlation with the distribution of news items per section by The Guardian and The New York Times. The results of this study provide a bridge between journalism and audience research and present evidence of the differences between readership in social and legacy media.

Context

As one of the most renowned online news platforms globally, The New York Times stands out for its exceptional ability to engage and connect with its readers. What sets this publication apart from others is its unique capacity to foster meaningful interactions with its audience. This dataset offers a wealth of information, presenting a valuable opportunity to analyze and gain insights from the extensive collection of news articles available through The New York Times. Explore the data and unlock the potential for in-depth analysis and understanding of news trends and patterns.

Content

This dataset contains a comprehensive collection of articles from The New York Times, spanning from January 1, 2000, to the present day. The dataset, titled "**The New York Times Articles Metadata**," includes over 2.1 million articles, capturing a vast range of topics and stories. It is important to note that this dataset is updated daily, ensuring that the latest articles from The New York Times are included, providing an up-to-date and evolving resource for analysis. If you want to know how I update the dataset daily. You can refer to my Scraping New York Times Articles (Daily Updated) this notebook for the code template.

Features

The dataset includes key features: 1. Abstract: A brief summary of the article's content. 2. Web URL: The article's web address. 3. Headline: The title or heading of the article. 4. Keywords: Tags associated with the article, providing insights into its content. 5. Pub Date: The publication date of the article. 6. News Desk: The department responsible for the article. 7. Section Name: The section or category of the article. 8. Byline: The author or authors of the article. 9. Word Count: The number of words in the article.

And many more features...

Inspiration

This dataset opens up various possibilities for analysis and exploration, such as:

1. Trend Analysis: Identify emerging topics and popular themes by analyzing the frequency of keywords and categories over time.
2. User Engagement: Explore reader comments and reactions to gain insights into public sentiment and opinions on various articles.
3. Sentiment Analysis: Analyze the emotional tone of news articles using sentiment analysis techniques on headings, snippets, or full text to understand public perception.
4. Content Recommendation: Build a recommendation system that suggests relevant articles based on user preferences, article content, and historical patterns.
5. Journalistic Styles: Examine the evolution of writing styles and journalistic preferences over time and across different sections or authors.
6. Data Visualization: Create visually compelling graphs, word clouds, and interactive dashboards to present meaningful insights and trends derived from the dataset.
7. Topic Modeling: Employ techniques such as Latent Dirichlet Allocation (LDA) to identify key topics and themes within the articles, providing a deeper understanding of the content.
8. Social Network Analysis: Uncover connections and influence networks between authors, articles, and readers, revealing patterns of collaboration and engagement.
9. Geographical Analysis: Explore geographical patterns by analyzing the distribution of news articles based on locations mentioned or covered.
10. Text Classification: Classify articles into different genres or categories using machine learning models to understand the diversity and distribution of content.

These are just a few examples to inspire you. Enjoy exploring the rich dataset and discovering valuable insights from The New York Times articles!

The Times of India, an English language daily newspaper, had an average issue readership of over ************ people across India in 2017. The newspaper was ranked as one of the top ten English dailies in the country that year in terms of readership figures.

The Times of India is one of the oldest English newspaper which is still in circulation in India, although under different names over the years since its first edition. The newspaper issued its first edition in 1838 as The Bombay Times and Journal of Commerce.

The News Dissemination Dataset is designed to predict the trends and success of news content spread across multiple digital platforms. It contains 1,000 samples of news articles with features reflecting user interactions, demographics, and content characteristics. The dataset is ideal for training predictive models to optimize content distribution strategies and improve audience engagement.

Key Features: Article_ID: Unique identifier for each news article. Platform: The platform on which the news article was shared (e.g., Facebook, Twitter, Instagram). Category: The content category of the news article (e.g., Politics, Sports, Entertainment). Shares: The number of times the article has been shared by users. Comments: The number of comments on the article. Clicks: The number of clicks on the article. Audience_Age: The average age of the audience interacting with the article. Audience_Location: The location of the audience (e.g., country or region). Dissemination_Success: A binary target variable indicating the success of news dissemination, based on the sum of Shares, Comments, and Clicks. This dataset is valuable for predictive modeling tasks related to digital news spread and trend analysis, offering a comprehensive view of the factors influencing the reach and engagement of online news content.

BASE YEAR	2024
HISTORICAL DATA	2019 - 2023
REGIONS COVERED	North America, Europe, APAC, South America, MEA
REPORT COVERAGE	Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
MARKET SIZE 2024	37.1(USD Billion)
MARKET SIZE 2025	38.8(USD Billion)
MARKET SIZE 2035	60.0(USD Billion)
SEGMENTS COVERED	Content Type, Subscription Model, Platform, Target Audience, Regional
COUNTRIES COVERED	US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA
KEY MARKET DYNAMICS	Digital subscription growth, Mobile readership increase, Advertising revenue transition, Content personalization demand, Sustainability focus
MARKET FORECAST UNITS	USD Billion
KEY COMPANIES PROFILED	Washington Post, Condé Nast, Meredith Corporation, Hachette Livre, Time Inc., Trinity Mirror, Axel Springer, The New York Times Company, News Corp, Gannett, Future Publishing, The Guardian, Hearst Communications, Schibsted, The Walt Disney Company, Bonnier Corporation
MARKET FORECAST PERIOD	2025 - 2035
KEY MARKET OPPORTUNITIES	Rise of mobile readership, Expansion of digital subscriptions, Increased demand for localized content, Integration of multimedia elements, Growth in advertising technology
COMPOUND ANNUAL GROWTH RATE (CAGR)	4.5% (2025 - 2035)

Context

This is a sample dataset that contains the reference information among various information sources such as TV, Newspaper and online articles.

Content

There are two datasets 1. InputFileEdges.csv contains the information about the edges between nodes. The fields in this dataset are as follows: (i) from: source (or) starting node id of the edge (ii) to: target (or) ending node id of the edge (iii) weight: the number of times they were connected (or) referenced each other (iv) type: the type of the link (hyperlink or mention) between these nodes

1. InputFileNodes.csv contains the information about the nodes. The fields in this dataset are as follows: (i) id: unique node id (ii) media: the media information of the node (e.g. NY Times, Washington Post, Wall Street Journal etc.) (iii) media.type: the type of the media (1 represents Newspaper; 2 represents TV; and 3 represents Online) (iv) type.label: the type of the media (Newspaper, TV, and Online) (v) audience.size: the audience size for each media

Abstract copyright UK Data Service and data collection copyright owner.

The National Readership Surveys are conducted quarterly either by the British Market Research Bureau or by Research Services Ltd. (1956 - 1959 Research Services Ltd; 1960 - 1967 British Market Research Bureau; 1968 - 1973 Research Services Ltd; 1974 - 1976 British Market Research Bureau; from 1977 onwards Research Services Ltd.). These surveys are designed principally to provide detailed information about the newspaper and magazine reading habits of the British people.
The sponsors are virtually all the leading newspaper proprietors and advertising agencies in the country; many leading manufacturers were also consulted over the plan of the surveys.
All surveys are substantially the same in form and content. Further details are available from the Archive upon application.
Main Topics:
Variables
Region and polling district; town size; % Labour to Conservative in constituency; % jurors to electorate in polling district*; classification of informant & household; social grade of household; household composition; age; age when finished full-time education; type of school/college last attended*; qualifications obtained*; accent of informant*; whether informant occupied; occupation of informant/head of household; smoking habits*; drinking habits*; camera usage*; use of cosmetics*; monthly general magazine readership; monthly women's magazine readership; national Sunday newspaper - detailed reading habits (where read, who pays, no. times read in week)*; daily national newspaper - detailed reading habits (where read, who pays, no. times read in day)*; cinema visiting; TV viewing; Radio Luxembourg listening*; national morning newspaper readership; provincial morning newspaper readership; evening newspaper readership; Sunday national newspaper readership; household composition (detailed); no. electors in household/institution*; home tenure; description of dwelling; household status of informant; sex and marital status; occupational details (i.e. industry, social grade etc.); income cohort; whether has PO savings or bank account; ownership of stocks shares or holdings in a unit trust; ownership of selected durable goods; car ownership; whether informant buys most petrol; selected publications, detailed reading habits (who pays, where read, no. times read)*; whether informant DIY person*; ownership of household pets (cats, dogs, birds)*; holiday away from home; holiday abroad; Sunday provincial newspaper readership.

*earlier surveys only.

Redmob delivers low-latency, privacy-compliant mobile digital audience data for clients who are interested to grow their presence in South and North America.

Built for adtech platforms, brands, agencies, and data buyers, Redmob gives you real-time access to rich mobile user profile data, enriched with 30+ custom attributes. From smarter segmentation to market insights, it supports strategies that drive real results, whether you're building audiences, optimizing campaigns, or analyzing consumer behavior.

Use Cases

Redmob equips you with the insights to boost performance, sharpen targeting, and understand your market at scale and in real time.

• Enrich first-party data to personalize campaigns
• Find new audiences similar to your most valuable users
• Set up smarter suppression and retargeting via DSPs
• Analyze market trends using behavior signals
• Segment users by demographics, app usage, device type, and more

Key Benefits

• 30+ enriched fields: app behavior, geo, device, and more
• A full 360° view of users across devices and platforms
• Low-latency access perfect for programmatic and real-time use cases
• Scalable infrastructure for large-scale audience segmentation
• Privacy-compliant, anonymized targeting at scale

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.

Social media platforms increasingly give users the option of ephemerality through settings that delete or hide posted content after a set period of time. Many individuals apply these settings to manage their posting history and, in turn, reduce concerns about self-presentation. Despite the growing popularity of this feature, few studies have empirically explored it. This study examines the Time Limit setting on WeChat Moments as an example and investigates how users using the Time Limit setting differ from nonusers in terms of personal characteristics (demographics, personality traits, psychological factors, and previous behavioral patterns) and social characteristics (audience size and audience diversity). Compared with nonusers, users using Time Limit setting scored significantly higher on posting frequency and privacy setting use and scored significantly lower on audience size. We also examine how personal and social characteristics vary between user groups with different degrees of ephemerality (i.e., low, medium, or high). Our findings show that users using the Time Limit setting who scored higher on measures of life changes, self-monitoring, posting frequency, and audience size and lower on perceived stress were more likely to opt for the low (i.e., 6months) rather than the medium (i.e., 1month) or high (i.e., 3days) degree of ephemerality. Our work contributes to the understanding of ephemerality settings on social media platforms and provides insights that help practitioners design more effective platforms.

Real‑Time Audience Sentiment Displays Market Outlook

According to our latest research, the global real-time audience sentiment displays market size reached USD 1.42 billion in 2024, with robust growth driven by the surging adoption of AI-driven analytics and interactive technologies across live events, broadcasting, and corporate environments. The market is projected to expand at a CAGR of 15.6% from 2025 to 2033, reaching a forecasted value of USD 5.46 billion by 2033. This dynamic growth is underpinned by increasing demand for immersive audience engagement solutions and the proliferation of digital transformation initiatives across multiple sectors, as per the latest research findings.

The primary growth driver for the real-time audience sentiment displays market is the escalating need for instant feedback mechanisms and data-driven decision-making in live event environments. As organizations and event organizers strive to enhance audience engagement and deliver personalized experiences, real-time sentiment analysis has become a critical tool. Technologies that can visualize and interpret live audience reactions are being rapidly integrated into concerts, sports arenas, and corporate conferences to provide actionable insights and foster deeper interaction. Furthermore, the integration of AI and machine learning algorithms has significantly improved the accuracy and reliability of sentiment analysis, enabling more nuanced understanding of audience mood and preferences. This technological evolution is encouraging a broader range of industries to invest in real-time sentiment display solutions, thus fueling market expansion.

Another significant factor contributing to market growth is the widespread adoption of hybrid and virtual event formats in the post-pandemic era. With audiences increasingly participating remotely, event organizers face the challenge of maintaining engagement and gauging sentiment in real-time. Real-time audience sentiment displays bridge this gap by aggregating and visualizing data from social media, live polls, and digital interactions, providing a comprehensive overview of audience sentiment regardless of physical location. This capability is especially valuable for broadcasters, educators, and corporate trainers, who rely on real-time feedback to adapt content dynamically and maximize impact. As the world continues to embrace remote and hybrid experiences, the demand for advanced sentiment display solutions is expected to remain strong.

The growing emphasis on data-driven marketing and audience analytics within the media and entertainment industry is also propelling the real-time audience sentiment displays market forward. Brands and content creators are increasingly leveraging sentiment data to tailor messaging, optimize advertising strategies, and enhance viewer satisfaction. The integration of sentiment displays with CRM systems and digital marketing platforms allows for granular analysis of audience reactions, enabling more effective targeting and improved ROI. Additionally, regulatory advancements and the standardization of data privacy frameworks are fostering greater trust and adoption of sentiment analysis technologies, further accelerating market growth.

Regionally, North America continues to dominate the real-time audience sentiment displays market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology providers, coupled with the early adoption of AI and IoT solutions, has cemented North America’s leadership position. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing investments in smart infrastructure, and a burgeoning events industry. Latin America, the Middle East, and Africa are gradually catching up, with rising demand for interactive technologies and expanding media landscapes. This regional diversification is creating new opportunities for market players and stimulating innovation across the industry.

Component Analysis

&

Redmob delivers low-latency, privacy-compliant mobile digital audience data for clients who are interested to grow their presence in Africa.

Built for adtech platforms, brands, agencies, and data buyers, Redmob gives you real-time access to rich mobile user profile data, enriched with 30+ custom attributes. From smarter segmentation to market insights, it supports strategies that drive real results, whether you're building audiences, optimizing campaigns, or analyzing consumer behavior.

Use Cases

Redmob equips you with the insights to boost performance, sharpen targeting, and understand your market at scale and in real time.

• Enrich first-party data to personalize campaigns
• Find new audiences similar to your most valuable users
• Set up smarter suppression and retargeting via DSPs
• Analyze market trends using behavior signals
• Segment users by demographics, app usage, device type, and more

Key Benefits

• 30+ enriched fields: app behavior, geo, device, and more
• A full 360° view of users across devices and platforms
• Low-latency access perfect for programmatic and real-time use cases
• Scalable infrastructure for large-scale audience segmentation
• Privacy-compliant, anonymized targeting at scale

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

CPP: Cost per point metric used to normalize value CPM: Cost per thousand impressions GRPs: Gross rating points for measuring campaign weight Reach: Percentage of target audience exposed to campaign Frequency: Average number of times audience members see ads

Overview

The study explores how Electronic Dance Music (EDM) artists leverage social media analytics to enhance audience engagement. As digital platforms dominate artist-fan interactions, understanding and optimizing engagement strategies has become essential. The research integrates quantitative metrics and qualitative insights from both EDM artists and their audiences, aiming to uncover data-driven strategies that can maximize social media impact.

Research Questions and Objectives

The research is driven by the following key questions:

1. How does social media analytics influence audience engagement in the EDM industry?
2. Which types of content generate the highest engagement for EDM artists?
3. What role does posting frequency play in shaping audience engagement?
4. Are there differences in engagement patterns across different platforms (e.g., Instagram, TikTok, YouTube)?
5. What social media strategies do successful EDM artists use compared to lesser-known artists?

Objectives:

• To quantify the impact of social media analytics on engagement levels.
• To identify best practices for EDM artists based on data-driven insights.
• To analyze audience behavior and preferences in online interactions.
• To provide strategic recommendations for EDM artists to maximize their digital presence.

Data Collection and Methodology

The research employs a mixed-methods approach, combining quantitative data from social media analytics with qualitative insights from surveys and interviews.

Data Sources:

1. Audience Data (CSV file)

   • Contains demographic details of EDM listeners.
   • Captures age group, country, gender, and preferred social media platforms.
   • Provides insights into where EDM fans are most active online.

2. Music Artists Data (CSV file)

   • Includes engagement metrics from various EDM artists’ social media accounts.
   • Tracks followers, likes, shares, comments, and overall engagement rates.
   • Differentiates between high-engagement and low-engagement artists.

3. Survey Responses

   • Captures audience preferences regarding social media interactions with artists.
   • Identifies factors influencing engagement (e.g., live sessions, Q&A, exclusive content).
   • Evaluates perceived authenticity and responsiveness of EDM artists.

4. Interviews with Industry Experts

   • Includes insights from social media managers and EDM producers.
   • Discusses strategic content planning and engagement tactics.

Data Analysis and Findings

After cleaning and analyzing the data, the study uncovered several critical insights:

A. Social Media Analytics and Audience Engagement

• EDM artists who actively track social media analytics experience higher audience engagement.
• A statistically significant correlation was found between regular content analysis and improved engagement rates (p < 0.05).
• Artists who used data-driven strategies (e.g., tracking the best posting times) saw a 30-40% higher interaction rate.

B. Content Type and Engagement Patterns

• Live performances and short-form videos generate the highest engagement across all platforms.
• Interactive content, such as Q&A sessions, behind-the-scenes footage, and polls, significantly increases audience retention.
• Memes and trending challenges help artists reach wider audiences but do not necessarily build long-term engagement.

C. Posting Frequency and Audience Retention

• Posting daily or several times a week yields the highest engagement.
• Overposting (multiple times per day) leads to audience fatigue, reducing interaction rates.
• Artists with a consistent posting schedule (rather than random uploads) perform better in engagement metrics.

D. Platform-Specific Engagement

• Instagram and TikTok drive the highest engagement among EDM fans.
• YouTube excels in long-term audience retention, with higher interaction rates on live sets and documentary-style content.
• Facebook engagement is declining, making it less relevant for EDM artists targeting younger audiences.

E. Audience Preferences and Geographic Insights

• The 18-24 age group dominates EDM's online audience.
• Fans from North America and Europe are the most engaged, while Asia is an emerging market with increasing engagement rates.
• Personalized engagement (e.g., responding to comments, direct messages) is highly valued by fans.

F. High vs. Low Engagement Artists

• High-engagement artists leverage data analytics to optimize content strategies.
• Low-engagement artists often lack structured social media planning, resulting in inconsistent interaction.

4. Key Business Implications

The study ...

Notes. The number of viewers is per ½ hour and may or may not be new/unique viewers. Audience viewing data from midnight to 5.59am was not available.Average TV viewing audience per ½ hour time period by age and time.