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.

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.

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.

In 2025, Facebook remained the most-used social platform for news in the United States, with ** percent of respondents reporting they accessed news on it. YouTube followed closely at ** percent, recording a slight increase from the previous year. X (formerly Twitter) saw the most notable growth, rising by ***** percent to ** percent.

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.

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

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.

We present Qbias, two novel datasets that promote the investigation of bias in online news search as described in

Fabian Haak and Philipp Schaer. 2023. 𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions. In Proceedings of ACM Web Science Conference (WebSci’23). ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3578503.3583628.

Dataset 1: AllSides Balanced News Dataset (allsides_balanced_news_headlines-texts.csv)

The dataset contains 21,747 news articles collected from AllSides balanced news headline roundups in November 2022 as presented in our publication. The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The AllSides balanced news aims to offer multiple political perspectives on important news stories, educate users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles. Overall, the dataset contains 10,273 articles tagged as left, 7,222 as right, and 4,252 as center.

To provide easier access to the most recent and complete version of the dataset for future research, we provide a scraping tool and a regularly updated version of the dataset at https://github.com/irgroup/Qbias. The repository also contains regularly updated more recent versions of the dataset with additional tags (such as the URL to the article). We chose to publish the version used for fine-tuning the models on Zenodo to enable the reproduction of the results of our study.

Dataset 2: Search Query Suggestions (suggestions.csv)

The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total number of topics. The topic tags include names, a wide range of political terms, agendas, and topics (e.g., "communism", "libertarian party", "same-sex marriage"), cultural and religious terms (e.g., "Ramadan", "pope Francis"), locations and other news-relevant terms. On average, the dataset contains 469 search queries for each topic. In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.

The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion" and "rank" represents the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".

We retrieved ten search query suggestions provided by the Google and Bing search autocomplete systems for the input of each of these root queries, without performing a search. Furthermore, we extended the root queries by the letters a to z (e.g., "democrats" (root term) >> "democrats a" (query input) >> "democrats and recession" (query suggestion)) to simulate a user's input during information search and generate a total of up to 270 query suggestions per topic and search engine. The dataset we provide contains columns for root term, query input, and query suggestion for each suggested query. The location from which the search is performed is the location of the Google servers running Colab, in our case Iowa in the United States of America, which is added to the dataset.

AllSides Scraper

At https://github.com/irgroup/Qbias, we provide a scraping tool, that allows for the automatic retrieval of all available articles at the AllSides balanced news headlines.

We want to provide an easy means of retrieving the news and all corresponding information. For many tasks it is relevant to have the most recent documents available. Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information. By providing the scraper we facilitate access to a recent version of the dataset for other researchers.

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

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.

This dataset consists of a collection of true and fake news related to COVID-19. The dataset consists of news between the period of December 2019- July 2020.

As reported by a survey conducted in 2025 on digital news consumption, about ** percent of respondents from India stated that they sourced their news ******, which included social media, making it a popular form of accessing news. In comparison, ** percent of respondents stated that they used print media as a news source during that period.

Dataset Card for "news-summary"

  Dataset Summary

Officially it was supposed to be used for classification but, can you use this data set to summarize news articles?

  Languages

english

  Citation Information

Acknowledgements Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018. Ahmed H, Traore I, Saad S. (2017) “Detection of Online… See the full description on the dataset page: https://huggingface.co/datasets/argilla/news-summary.

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.

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

News release produced by the Office for National Statistics (ONS)

Source agency: Office for National Statistics

Designation: Supporting material

Language: English

Alternative title: Media

Techsalerator’s News Event Data in North America offers a comprehensive and detailed dataset designed to provide businesses, analysts, journalists, and researchers with a thorough view of significant news events across North America. 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, providing valuable insights into regional developments, economic shifts, political changes, and cultural events.

Key Features of the Dataset: Extensive Coverage:

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

News events are categorized into various types such as 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 that users have access to up-to-date news and can stay informed about recent developments as they happen. Geographic Segmentation:

Events are tagged with their respective countries and territories within North America. 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 conduct comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. 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. North American Countries and Territories Covered: Countries: Canada Mexico United States Territories: American Samoa (U.S. territory) French Polynesia (French overseas collectivity; included for regional relevance) Guam (U.S. territory) New Caledonia (French special collectivity; included for regional relevance) Northern Mariana Islands (U.S. territory) Puerto Rico (U.S. territory) Saint Pierre and Miquelon (French overseas territory; geographically close to North America and included for regional comprehensiveness) Wallis and Futuna (French overseas collectivity; included for regional relevance) 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 North America, 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 North American news and events. Techsalerator’s News Event Data in North America is a crucial resource for accessing and analyzing significant news events across the continent. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Dataset

This dataset was created by Shine K George

Released under Data files © Original Authors

Contents

The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.Data description: This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).The data structure is tabulated as follows:Text: The main content.Dimension: Descriptive category of the text.Biased_Words: A compilation of words regarded as biased.Aspect: Specific sub-topic within the main content.Label: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutralToxicity: Indicates the presence (True) or absence (False) of bias.Identity_mention: Mention of any identity based on words match.Annotation SchemeThe labels and annotations in the dataset are generated through a system of Active Learning, cycling through:Manual LabelingSemi-Supervised LearningHuman VerificationThe scheme comprises:Bias Label: Specifies the degree of bias (e.g., no bias, mild, or strong).Words/Phrases Level Biases: Pinpoints specific biased terms or phrases.Subjective Bias (Aspect): Highlights biases pertinent to content dimensions.Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.List of datasets used : We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.We also utilize publicly available data from the following links. Our Attribution to others.MBIC (media bias): Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). https://zenodo.org/records/4474336Hyperpartisan news: Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. https://huggingface.co/datasets/hyperpartisan_news_detectionToxic comment classification: Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge.Jigsaw Unintended Bias: Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification.Age Bias : Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)Multi-dimensional news Ukraine: Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. https://zenodo.org/records/3885351#.ZF0KoxHMLtVSocial biases: Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). https://maartensap.com/social-bias-frames/Goal of this dataset :We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.If you use this dataset, please cite us.Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under CC BY-NC 4.0

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