Stay ahead with our comprehensive News Dataset, designed for businesses, analysts, and researchers to track global events, monitor media trends, and extract valuable insights from news sources worldwide.

Dataset Features

News Articles: Access structured news data, including headlines, summaries, full articles, publication dates, and source details. Ideal for media monitoring and sentiment analysis. Publisher & Source Information: Extract details about news publishers, including domain, region, and credibility indicators. Sentiment & Topic Classification: Analyze news sentiment, categorize articles by topic, and track emerging trends in real time. Historical & Real-Time Data: Retrieve historical archives or access continuously updated news feeds for up-to-date insights.

Customizable Subsets for Specific Needs Our News Dataset is fully customizable, allowing you to filter data based on publication date, region, topic, sentiment, or specific news sources. Whether you need broad coverage for trend analysis or focused data for competitive intelligence, we tailor the dataset to your needs.

Popular Use Cases

Media Monitoring & Reputation Management: Track brand mentions, analyze media coverage, and assess public sentiment. Market & Competitive Intelligence: Monitor industry trends, competitor activity, and emerging market opportunities. AI & Machine Learning Training: Use structured news data to train AI models for sentiment analysis, topic classification, and predictive analytics. Financial & Investment Research: Analyze news impact on stock markets, commodities, and economic indicators. Policy & Risk Analysis: Track regulatory changes, geopolitical events, and crisis developments in real time.

Whether you're analyzing market trends, monitoring brand reputation, or training AI models, our News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** 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 ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** 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.

Data Set Information: * The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls. * Acquisition date: January 8, 2015 * The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method. See their article for more details on how the relative performance values were set. ##Attribute Information: Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field) 0. url: URL of the article (non-predictive) 1. timedelta: Days between the article publication and the dataset acquisition (non-predictive) 2. n_tokens_title: Number of words in the title 3. n_tokens_content: Number of words in the content 4. n_unique_tokens: Rate of unique words in the conte

Social media was by far the most popular news platform among 18 to 34-year-olds in the United States, with 47 percent of respondents to a survey held in August 2022 saying that they used social networks for news on a daily basis. By comparison, adults over 65 years old mostly used network news to keep up to date.

The decline of newspapers In the past, the reasons to regularly go out and purchase a print newspaper were many. Used not only for news but also apartment hunting, entertainment, and job searches (among other things), newspapers once served multiple purposes. This is no longer the case, with first television and then the internet taking care of consumer needs once covered by printed papers. Indeed, the paid circulation of daily weekday newspapers in the United States has fallen dramatically since the 1980s with no sign of future improvement.

News consumption habits

A survey on news consumption by gender found that 50 percent of women use either online-only news sites or social media for news each day, and 51 percent of male respondents said the same. Social media was by far the most used daily news platform among U.S. Millennials, and the same was true of Gen Z. One appeal of online news is that it often comes at no cost to the consumer. Paying for news found via digital outlets is not yet commonplace in the United States, with only 21 percent of U.S. consumers responding to a study held in early 2021 reporting having paid for online news content in the last year.

Introduction

Fake News Statistics: Fake news has become a major problem in today's digital age in recent years. It spreads quickly through social media and other online platforms, often misleading people. Fake news spreads faster than real news, thus creating confusion and mistrust among global people. In 2024, current statistics and trends reveal that many people have encountered fake news online, and many have shared it unknowingly.

Fake news affects public opinion, political decisions, and even relationships. This article helps us understand how widespread it is and helps us address several issues more effectively. Raising awareness and encouraging critical thinking can reduce its impact, in which reliable statistics and research are essential for uncovering the truth and stopping the spread of false information. Everyone plays a role in combating fake news.

Get access to a comprehensive and structured dataset of BBC News articles, freshly crawled and compiled in February 2023. This collection includes 1 million records from one of the world’s most trusted news organizations — perfect for training NLP models, sentiment analysis, and trend detection across global topics.

💾 Format: CSV (available in ZIP archive)

📢 Status: Published and available for immediate access

Use Cases

• Train language models to summarize or categorize news

• Detect media bias and compare narrative framing

• Conduct research in journalism, politics, and public sentiment

• Enrich news aggregation platforms with clean metadata

• Analyze content distribution across categories (e.g. health, politics, tech)

This dataset ensures reliable and high-quality information sourced from a globally respected outlet. The format is optimized for quick ingestion into your pipelines — with clean text, timestamps, image links, and more.

Need a filtered dataset or want this refreshed for a later date? We offer on-demand news scraping as well.

👉 Request access or sample now

Techsalerator’s News Event Data in Asia offers a detailed and expansive dataset designed to provide businesses, analysts, journalists, and researchers with comprehensive insights into significant news events across the Asian continent. This dataset captures and categorizes major events reported from a diverse range of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable perspectives on regional developments, economic shifts, political changes, and cultural occurrences.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide range of sources such as company press releases, industry-specific news outlets, blogs, PR sites, and traditional media. This broad coverage ensures a diverse array of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly find and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most current events, ensuring users have access to the latest news and can stay informed about recent developments as they happen. Geographic Segmentation:

Events are tagged with their respective countries and regions within Asia. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps users understand the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into the evolution of news events. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Asian Countries and Territories Covered: Central Asia: Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan East Asia: China Hong Kong (Special Administrative Region of China) Japan Mongolia North Korea South Korea Taiwan South Asia: Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Southeast Asia: Brunei Cambodia East Timor (Timor-Leste) Indonesia Laos Malaysia Myanmar (Burma) Philippines Singapore Thailand Vietnam Western Asia (Middle East): Armenia Azerbaijan Bahrain Cyprus Georgia Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey (partly in Europe, but often included in Asia contextually) United Arab Emirates Yemen Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and identify emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Asia, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Asian news and events. Techsalerator’s News Event Data in Asia is a crucial resource for accessing and analyzing significant news events across the continent. By offering detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

The Fox News Dataset is a comprehensive collection of over 1 million news articles, offering an unparalleled resource for analyzing media narratives, public discourse, and political trends. Covering articles up to the year 2023, this dataset is a treasure trove for researchers, analysts, and businesses interested in gaining deeper insights into the topics and trends covered by Fox News.

Key Features of the Fox News Dataset

• Extensive Coverage: Contains more than 1 million articles spanning various topics and events up to 2023.
• Research-Ready: Perfect for text classification, natural language processing (NLP), and other research purposes.
• Format: Provided in CSV format for seamless integration into analytical and research tools.

Why Use This Dataset?

This large dataset is ideal for:

• Text Classification: Develop machine learning models to classify and categorize news content.
• Natural Language Processing (NLP): Conduct sentiment analysis, keyword extraction, or topic modeling.
• Media and Political Research: Analyze media narratives, public opinion, and political trends reflected in Fox News articles.
• Trend Analysis: Identify shifts in public discourse and media focus over time.

Explore More News Datasets

Discover additional resources for your research needs by visiting our news dataset collection. These datasets are tailored to support diverse analytical applications, including sentiment analysis and trend modeling.

The Fox News Dataset is a must-have for anyone interested in exploring large-scale media data and leveraging it for advanced analysis. Ready to dive into this wealth of information? Download the dataset now in CSV format and start uncovering the stories behind the headlines.

Data Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com .

Citation

Please cite our work as

@article{shahi2021overview,
 title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
 author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
 journal={Working Notes of CLEF},
 year={2021}
}

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English.

Subtask 3A: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:

• False - The main claim made in an article is untrue.

• Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

• True - This rating indicates that the primary elements of the main claim are demonstrably true.

• Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Subtask 3B: Topical Domain Classification of News Articles (English) Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

Task 3a

• ID- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• our rating - class of the news article as false, partially false, true, other

Task 3b

• public_id- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• domain - domain of the given news article(applicable only for task B)

Output data format

Task 3a

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source:

• Fakenews Classification Datasets
• Fake News Detection Challenge KDD 2020
• FakeNewsNet

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.

Evaluation Metrics

This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.

Submission Link: https://competitions.codalab.org/competitions/31238

Related Work

• Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
• G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
• Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

In this dataset have to part combined namely fake news and true news. fake news collected from Kaggle and some true news collected form IEEE Data port. Therefor some true news data required to optimize with the fake news. After that i have collect some true news from different trusted online site. Finally i have concat the Fake and True news as a single dataset for the purpose to help the Researchers further if they want to research by taken this topic.

In 2024, ** percent of respondents to a survey in the United States said that they used Facebook for news. Facebook remains the leading social media network for news consumption among U.S. consumers. In second place was YouTube, with ** percent, marking a jump from the previous year.

By downloading the data, you agree with the terms & conditions mentioned below:

Data Access: The data in the research collection may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes.

Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.

We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.

Citation

Please cite our work as

@InProceedings{clef-checkthat:2022:task3, author = {K{"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas}, title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection", year = {2022}, booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum", series = {CLEF~'2022}, address = {Bologna, Italy},}

@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English and German.

Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Our definitions for the categories are as follows:

False - The main claim made in an article is untrue.

Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

True - This rating indicates that the primary elements of the main claim are demonstrably true.

Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Cross-Lingual Task (German)

Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

ID- Unique identifier of the news article

Title- Title of the news article

text- Text mentioned inside the news article

our rating - class of the news article as false, partially false, true, other

Output data format

public_id- Unique identifier of the news article

predicted_rating- predicted class

Sample File

public_id, predicted_rating 1, false 2, true

IMPORTANT!

We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.

Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498

Related Work

Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf

G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14

Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.

Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.

Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham.

Academic article descriptive statistics.

According to the results of a global survey held in early 2025, among selected major media markets, consumers in Brazil were the least likely to use print media to get their news, with just ten percent reporting consuming print news compared to 39 percent of respondents from India. Meanwhile, news from social media was most popular in South Africa, and TV consumption was highest in Germany.

The summary statistics by North American Industry Classification System (NAICS 51111) for Newspaper publishers, which include all members under Industry Summary statistics, every two years (dollars) for five years of data.

Dataset

This dataset was created by Akash_Sandhu4x4

Released under MIT

Contents

Context

The dataset contains the list of COVID Fake News/Claims which is shared all over the internet.

Content

1. Headlines: String attribute consisting of the headlines/fact shared.
2. Outcome: It is binary data where 0 means the headline is fake and 1 means that it is true.

Inspiration

In many research portals, there was this common question in which the combined fake news dataset is available or not. This led to the publication of this dataset.

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ag_news_subset', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

The share of news consumers who actively try to avoid news was highest in Greece and Bulgaria as of early 2023, with ** percent of respondents from each country saying they deliberately chose not to engage with news. Active news avoidance was also common in Argentina, Poland, and the United Kingdom, where the share of respondents saying they did so was over ** percent.

Analysis of ‘News broker statistics 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/3ce641c3-83fd-4122-acf1-f13ebcf77f69 on 15 January 2022.

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

The statistics provide information on the number of messages transmitted via the news blog for each service package.

The new broker is a central intermediation office that can be imagined as a “data hub”. It supports and optimises technical and organizational communication processes on behalf of a wide range of IT processes. The focus of the tasks is therefore the safe “machine (specialised procedure) to machine (technical procedure) communication” for XÖV messages.

The news broker offers various services (broker services), such as XMeld data transmissions or XDOMEA electronic registration certificate.

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