The dataset contains detailed information on some of the most popular English media channels on Youtube. From channel overview to statistics of the top 50 videos of each channel, here is a description of all the columns of the two datasets.

Mainstream Media Statistics

1. channelName: name of the channel on Youtube
2. id: The channel ID in Youtube
3. subscribers: subscriber count (up till 14/7/2023)
4. total views: total views of all the videos of the channel (up till 14/7/2023)
5. total videos: total number of videos of the channel (up till 14/7/2023)
6. created date: The date where the channel is created
7. description: description of the channel in their description page
8. playlistId: The id of the channel's video list

Top50_viewed_video_from_each_channels

1. Video Id: The ID of the video on Youtube
2. Channel Title: The channel name of the video
3. Title: Title of the video
4. publishedAt: When the video is published
5. categoryId: The category ID of Youtube (You may reference at https://mixedanalytics.com/blog/list-of-youtube-video-category-ids/)
6. description: The description of the video
7. viewCount: The total number of views of that video (up till 14/7/2023)
8. likeCount: The total number of likes of that video (up till 14/7/2023)
9. commentCount: The total number of comments of that video (up till 14/7/2023)
10. duration: The duration of that video

Inspirations

Data is scraped using Youtube API, feel free to use the data as long as it copes with the term of uses of Youtube. Something you can do with the dataset may be to analysis what news are of people's interest or to watch some of the most viewed news in the world to stay close with the society.

Weekly TV Viewership data by BARC at https://www.barcindia.co.in/data-insights. Currently shared are ratings for Channels ranking, Advertisers rating and brand ranking in seperate csv files.

Context

Self updating dataset. It collects RSS Feeds from BBC News using a Kernel: https://www.kaggle.com/gpreda/bbc-news-rss-feeds. The Kernel is run with a fixed frequency and the dataset is updated using the output of the Notebook.

Content

BBC News RSS Feeds. The data contains the following columns: - title
- pubDate
- guid
- link
- description

Collection method

Uses requests_html and BeautifulSoup to collect RSS Feeds from BBC News site.

Acknowledgements

The content is proprietary of BBC

Inspiration

Use the data to analyze the sentiment of news, from title and description

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Many Americans consume aligned partisan media, which scholars worry contributes to polarization. Many propose encouraging these Americans to consume cross-cutting media to moderate their attitudes. However, motivated reasoning theory posits that exposure to cross-cutting media could backfire, exacerbating polarization. Building on theories that sustained exposure to novel information can overcome motivated reasoning and that partisan sources on opposite sides cover distinct information, we argue that sustained consumption of cross-cutting media leads voters to learn uncongenial information and moderate their attitudes in covered domains. To test this argument, we used data on actual TV viewership to recruit a sample of regular Fox News viewers and incentivized a randomized treatment group to watch CNN instead for a month. Contrary to predictions from motivated reasoning, watching CNN caused substantial learning and moderated participants' attitudes in covered domains. We close by discussing challenges partisan media may pose for democracy.

Comprehensive ranking dataset of the top 100 YouTube channels in the News & Politics category. This dataset features 100 channels with detailed statistics including subscriber counts, total video views, video count, and global rankings. The leading channel has 74,400,000 subscribers and 42,602,103,612 total views. Each entry includes comprehensive metrics to analyze channel performance, growth trends, and competitive positioning. This dataset is regularly updated to reflect the latest YouTube channel statistics and ranking changes, providing valuable insights for content creators, marketers, and researchers analyzing YouTube ecosystem trends and channel performance benchmarks.

Dataset

This dataset was created by Thor God of Thunder

Contents

Percentage of persons aged 15 years and over by trust in media and main source of news, by gender, for Canada, regions and provinces.

(WELFake) is a dataset of 72,134 news articles with 35,028 real and 37,106 fake news. For this, authors merged four popular news datasets (i.e. Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent over-fitting of classifiers and to provide more text data for better ML training.

Dataset contains four columns: Serial number (starting from 0); Title (about the text news heading); Text (about the news content); and Label (0 = fake and 1 = real).

There are 78098 data entries in csv file out of which only 72134 entries are accessed as per the data frame.

Published in: IEEE Transactions on Computational Social Systems: pp. 1-13 (doi: 10.1109/TCSS.2021.3068519).

Aktivitäten in der Freizeit. Internet- und Mobiltelefonnutzung. Vor- und Nachteile der Internet- und Mobiltelefonnutzung. Themen: Häufigkeit von Freizeitaktivitäten (Sport, Kino, Fernsehen, Gaststättenbesuche, Hobby, Informationssuche, Freundeskontakte); Häufigkeit aktiver Teilnahme an Vereinsaktivitäten; Personenvertrauen; Häufigkeit der Internetnutzung zu privaten Zwecken; Art der Internetnutzung; Verbesserungen durch das Internet: Bekanntschaften machen, Verwaltung von Finanzen, Umgang mit Behörden, Erhalt gesundheitsbezogener Informationen, Ausführung der Arbeit, Tätigung von Einkäufen, Gelegenheit zum Lernen, Ausüben von Hobbies, Information über aktuelle Themen, Kontakte zu Familienmitgliedern und Freunden sowie Gelegenheit zum kulturellen Austausch; Nachteile und Vorteile durch eine Nicht-Nutzung des Internets: geringere Gelegenheit zur persönlichen Kontaktpflege, Nachteile in Bezug auf die beruflichen Perspektiven, Risiko, altmodisch zu werden, weniger Offenheit gegenüber der Außenwelt, geringere Informiertheit, mehr Zeit für Freunde und Familie, geringes Risiko Opfer von Online-Betrug zu werden, höherer Schutz der persönlichen Daten, schlechtere Erreichbarkeit zu beruflichen Zwecken, geringeres Risiko der Frustration durch komplizierte Technologien; Internetnutzung über Freunde oder Verwandte; Nutzung eines Mobiltelefons; Vorteile der Mobiltelefonnutzung: Kontaktpflege mit Familie und Freunden, bessere Informiertheit, Organisation der Freizeit, Austausch von Ideen und Materialien, gesteigertes Sicherheitsgefühl, zu Arbeitszwecken; Auswirkungen durch eine Nicht-Nutzung von Mobiltelefonen: verpasste Gelegenheiten der Kontaktpflege, schlechtere Erreichbarkeit, Kostenersparnis, weniger Stress. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Gewichtungsfaktor. Attitudes towards the benefits of internet and mobile phone use. Topics: frequency of the following leisure activities: sport, cultural activities, watch TV, go out, pursue a hobby, keep oneself informed, meet friends; frequency of participating in activities of organisations; trust in other people; frequency of internet use; online activities: send or receive emails or instant messages, purchase goods or services, internet banking, download multimedia content or software, use electronic forms of public administration, learn, use social networks, look for information, read or watch news, upload content, do daily work, transfer content to other devices; assessment of the improvement in selected areas due to the internet: opportunity to meet new people, way to manage finances, way to deal with public authorities, way to get health-related information, way to perform job, way to shop, opportunity to learn, way to pursue hobbies, capability to be informed, personal relationships, opportunity to access culture; attitude towards the following statements on people that don’t use the internet: miss opportunity of greater contact to people, are at disadvantage in career prospects, are at risk of becoming old-fashioned, miss opportunity of finding good bargains, are less open to the world, are less informed, have more time, are not at risk of online fraud, are not at risk of others finding out personal information about them, are less reachable for professional purposes, avoid frustration of dealing with complicated technologies; asked other person in the last year to send email, get information from the internet or make online purchase for oneself; frequency of mobile phone use; assessment of the improvement in selected areas due to mobile phones: keep contact with people, capability to be informed, way to manage free time, share content, feel more secure, work; attitude towards the following statements on people that don’t use mobile phones: miss opportunity of greater contact to people, are less reachable, are saving money, have less stress. Demography: sex; age; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; weighting factor.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

Data used for analyses. (SAV)

Initial Jobless Claims in the United States decreased to 216 thousand in the week ending November 22 of 2025 from 222 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Bürger und Staat: Eigenschaften eines guten Bürgers; Einstellung zur Versammlungsfreiheit für religiöse Extremisten, Revolutionäre und Ethnozentristen; soziale und politische Partizipation; Häufigkeit der Mediennutzung für politische Informationen; Anzahl direkter Kontaktpersonen an einem typischen Wochentag (gruppiert); aktive bzw. passive Mitgliedschaften in ausgewählten Gruppen oder Vereinigungen (politische Partei, Gewerkschaft, Kirche oder andere religiöse Organisation, Sportgruppe, Freizeitgruppe oder Kulturgruppe, andere freiwillige Vereinigung); Wichtigkeit verschiedener Bürgerrechte; Beeinflussbarkeit des politischen Systems und politische Informiertheit; Einschätzung von Einflussmöglichkeiten auf die nationale Politik bzw. Gesetzgebung (political efficacy); Politikinteresse; Links-Rechts-Selbsteinstufung; politisches Vertrauen: Vertrauen in Angehörige der Regierung und in Politiker; Häufigkeit des Gefühls der Übervorteilung und der fairen Behandlung durch Menschen; allgemeines Personenvertrauen; Häufigkeit politischer Diskussionen mit Freunden, Verwandten oder Kollegen; politische Meinungsführerschaft; Meinung zu politischen Parteien und Volksentscheid (politische Parteien ermutigen Menschen zu politischer Aktivität, politische Parteien bieten keine wirklichen Wahlmöglichkeiten, Volksentscheid als guter Weg bei wichtigen politischen Fragen); Bewertung der letzten nationalen Wahlen im Hinblick auf den Wahlprozess und die Möglichkeiten der Kandidaten und Parteien den Wahlkampf zu führen; Einschätzung der Bürgernähe des öffentlichen Dienstes im Land; Einschätzung der Verbreitung von Korruption im öffentlichen Dienst; Funktionieren der Demokratie im Land derzeit, vor zehn Jahren und zukünftig in 10 Jahren. Optional: Häufigkeit der Rezeption politischer Inhalte einer Tagesszeitung, politischer Nachrichten im Fernsehen und im Radio sowie der Internetnutzung für politische Informationen. Demographie: Geschlecht; Alter; Geburtsjahr; Jahre der Schulbildung; Bildung (länderspezifisch); höchster Abschluss; Erwerbsstatus; Wochenarbeitszeit; Arbeitsverhältnis; Zahl der Beschäftigten (Unternehmensgröße); Weisungsbefugnis; Anzahl der Mitarbeiter für die Weisungsbefugnis besteht; Art des Unternehmens: Profit vs. Non-Profit und öffentlich vs. privat; Beruf (ISCO-08); Hauptbeschäftigungsstatus; Zusammenleben mit einem Partner; Gewerkschaftsmitgliedschaft; Religionszugehörigkeit oder Konfession (länderspezifisch); Konfessionsgruppen; Kirchgangshäufigkeit; Oben-Unten-Selbsteinstufung; Wahlbeteiligung bei der letzten allgemeinen Wahl und gewählte länderspezifische Partei; Links-Rechts-Einstufung der gewählten Partei; Selbsteinschätzung der Zugehörigkeit zu einer ethnischen Gruppe 1 und 2 (länderspezifisch); Anzahl der Kinder; Anzahl der Kleinkinder; Haushaltsgröße; Einkommen des Befragten (länderspezifisch); Familieneinkommen (länderspezifisch); Geburtsland von Vater und Mutter; Familienstand; Urbanisierungsgrad; Region (länderspezifisch). Angaben zum Ehepartner bzw. Partner hinsichtlich: Erwerbsstatus; Wochenarbeitszeit; Arbeitsverhältnis; Weisungsbefugnis; Beruf (ISCO-08); Hauptbeschäftigungsstatus. Zusätzlich verkodet wurde: Interviewdatum; Case substitution flag; Erhebungsmethode; Gewicht; Gruppe (Mixed-Mode-Methoden der Datenerhebung). Citizen and state. Topics: Qualities of a good citizen; attitude towards the right of public meetings for religious extremists, people who want to overthrow the government by force, or people prejudiced against any racial or ethnic group; social and political participation; frequency of media use to get political news or information; number of people the respondent has contact with in a typical week day; active and passive memberships in different kinds of groups or associations (a political party, a trade union, business, or professional association, a church or other religious organization, a sports, leisure or cultural group); importance of different people´s rights in democracy (scale); suggestibility of the political system and personal level of information about politics; estimation of political influence possibilities (political efficacy): likeliness of counter-action against an unjust law and expected chance of serious attention to people´s demand; interest in politics; left-right self-placement; trust in politicians and people in government; estimation of people’s fairness; frequency of political discussions with friends; opinion leadership in politics; political parties encourage people to become active in politics; real policy choice between parties; attitude towards a referendum as a good way to decide important political questions; level of honesty and fairness in the last national election; commitment to serve people in public service; corruption in the public service; functioning of democracy in the country at present, in the past and in the future.Optional: frequencies of media use (read the political content of a newspaper, watch political news on television, listen to political news on the radio, use the Internet to get political news or information). Demography: Sex; age; year of birth; years in school; education (country specific); highest completed degree; work status; hours worked weekly; employment relationship; number of employees; supervision of employees; number of supervised employees; type of organization: for-profit vs. non profit and public vs. private; occupation (ISCO-08); main employment status; living in steady partnership; union membership; religious affiliation or denomination (country specific); groups of religious denominations; attendance of religious services; top-bottom self-placement; vote in last general election; country specific party voted in last general election; party voted (left-right); self-assessed affiliation of ethnic group 1 and 2 (country specific); number of children; number of toddlers; size of household; earnings of respondent (country specific); family income (country specific); father´s and mother´s country of birth; marital status; place of living: urban – rural; region (country specific). Information about spouse/partner on: Work status; hours worked weekly; employment relationship; supervises other employees; occupation (ISCO-08); main employment status. Additionally encoded: Date of interview; case substitution flag; weight; mode of data collection; group (participation in data collection mixed mode experiment).

This is a direct clone of Third Eye Data: TV News Archive chyrons from archive.org.

Introduction

The TV News Archive's Third Eye project captures the chyrons–or narrative text–that appear on the lower third of TV news screens and turns them into downloadable data and a Twitter feed for research, journalism, online tools, and other projects. At project launch (September 2017) we are collecting chyrons from BBC News, CNN, Fox News, and MSNBC–more than four million collected over just two weeks. Chyrons have public value because:

• Breaking news often appears on chyrons before TV newscasters begin reporting or video is available, whether it's a hurricane or a breaking political story.
• Which chyrons a TV news network chooses to display can reveal editorial decisions that can inform public understanding of how news is filtered for different audiences.
• Providing chyrons as data–and also on Twitter–in near real-time can serve as a alert system, showing how TV news stations are reporting the news. Often the chyrons are ahead of the general conversation on Twitter.

Data Source

The work of the Internet Archive's TV architect Tracey Jaquith, the Third Eye project applies OCR to the "lower thirds" of TV cable news screens to capture the text that appears there. The chyrons are not captions, which provide the text for what people are saying on screen, but rather are text narrative that accompanies news broadcasts.

Filtering

Created in real-time by TV news editors, chyrons sometimes include misspellings. The OCR process also frequently adds another element where text is not rendered correctly, leading to entries that may be garbled. To make sense out of the noise, Jaquith applies algorithms that choose the most representative chyrons from each channel collected over 60-second increments. This cleaned-up feed is what fuels the Twitter bots that post which chyrons are appearing on TV news screens.

This Kaggle dataset only provides data from the filtered feed.

Data Notes

• Dates/times are in UTC (Coordinated Universal Time).
• "Duration" column is in seconds–the amount of time that particular chyron appeared on the screen.
• To view clips in context on the TV News Archive, paste "https://archive.org/details/" before the field that begins with a channel name. For example, "FOXNEWSW_20170919_100000_FOX_Friends/start/792" becomes "https://archive.org/details/FOXNEWSW_20170919_100000_FOX_Friends/start/792"

A global survey conducted in the third quarter of 2024 found that the main reason for using social media was to keep in touch with friends and family, with over 50.8 percent of social media users saying this was their main reason for using online networks. Overall, 39 percent of social media users said that filling spare time was their main reason for using social media platforms, whilst 34.5 percent of respondents said they used it to read news stories. Less than one in five users were on social platforms for the reason of following celebrities and influencers.

              The most popular social network

              Facebook dominates the social media landscape. The world's most popular social media platform turned 20 in February 2024, and it continues to lead the way in terms of user numbers. As of February 2025, the social network had over three billion global users. YouTube, Instagram, and WhatsApp follow, but none of these well-known brands can surpass Facebook’s audience size.
              Moreover, as of the final quarter of 2023, there were almost four billion Meta product users.

              Ever-evolving social media usage

              The utilization of social media remains largely gratuitous; however, companies have been encouraging users to become paid subscribers to reduce dependence on advertising profits. Meta Verified entices users by offering a blue verification badge and proactive account protection, among other things. X (formerly Twitter), Snapchat, and Reddit also offer users the chance to upgrade their social media accounts for a monthly free.

What respondent experiences as most important in life / how many times respondent is watching tv, to which stations / motivations of respondent to watch and listen to tv and radio / watching alone or with other people / which kind of programs is respondent watching / if respondent using a video recorder / if respondent is member of a broadcasting corporation / how many radio's are used in house / how often respondent is listening to the radio, to which channels / r.'s attitude towards politics / if respondent is reading papers, and which papers about which subjects / how much time it takes to read a paper (for respondent) / which kind of books respondent reads / which kind of languages respondent speaks / which kind of subjects respondents interest, and how much / r.'s opinion about local news and local media / r.'s opinion about his-her financial situation, now and in future / r.'s opinion about living in this municipality / r.'s activities in free time / r.'s contacts with local people, family, neighbours and colleagues / r's makes time-table in quarters of an hour of exact activities. Background variables: basic characteristics/ residence/ housing situation/ household characteristics/ characteristics of parental family/household/ place of work/ occupation/employment/ income/capital assets/ education/ politics/ religion/ consumption of durables/ readership, mass media, and 'cultural' exposure/ organizational membership

Non Farm Payrolls in the United States increased by 119 thousand in September of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Comprehensive YouTube channel statistics for Arabsiyo News, featuring 250,000 subscribers and 105,929,547 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in GB. Track 6,810 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

📊 Dataset Overview — Pakistani News Media Synthetic Dataset

This dataset represents synthetic media data from Pakistani news outlets, combining information from television, newspapers, and social media. It simulates the activity of journalists, news channels, political influences, audience responses, and media economics.

The purpose of this dataset is to support analysis in:

📰 Media bias detection

📉 Sentiment and political polarization studies

📺 Audience engagement and viewership trends

💰 Revenue, advertisement impact, and airtime analytics

🤖 Machine learning tasks (classification, prediction, clustering, anomaly detection)

✅ Key Features of the Dataset Category Description Journalistic Info Journalist name, news channel, newspaper, political affiliation Geographic Attributes Region, city of reporting (e.g., Sindh, Islamabad, Lahore) Content Details Headline, topic (Politics, Sports, Health, etc.), language Audience Metrics Ratings, viewership numbers, social media shares & interactions Sentiment & Bias Sentiment score (-1 to +1), bias score (0–10), controversy flag Economic Indicators Revenue, advertisement spend, airtime duration Data Quality Indicators Missing data flag, publication date 📁 Column Description Column Name Description ID Unique record identifier Journalist Name of journalist or anchorperson Channel TV news channel (e.g., ARY News, Geo News) Newspaper Newspaper name (e.g., The News, Dawn) Region / City Location of reporting Topic News category (Politics, Health, Sports, etc.) Headline Title or summary of the news item Airtime Duration of news coverage (sec/min) Ratings Popularity or viewership score Revenue Estimated earnings (numeric/text e.g., “5 million”, “50 lakh”) AdSpend Advertising spend linked to the story SentimentScore Sentiment polarity of content (-0.5 to 1.0) BiasScore Bias intensity (0 = neutral, 10 = highly biased) Viewership Number of viewers watching the broadcast Shares Number of times shared on social media SocialMediaInteractions Combined likes, comments, shares, etc. ControversyFlag Whether story is controversial (Yes/No/1) MissingDataFlag 1 = missing values, 0 = complete data Date Publication date (YYYY-MM-DD) Language Language of publication (Urdu, English) PoliticalAffiliation Media stance (Pro-Govt, Opposition, Neutral) 🎯 Possible Use Cases

Media Bias & Sentiment Analysis

Predicting viral content or controversy

Time-series trend analysis of political coverage

Revenue and AdSpend forecasting

Impact of journalist or channel bias on public engagement

⚠️ Notes

This dataset is synthetic, created for educational and research purposes.

No real identities or confidential media data are used.

Ideal for students, researchers, journalists, and data scientists.