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.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

Inflation Rate in the United States increased to 2.70 percent in June from 2.40 percent in May of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Access a wealth of information, including article titles, raw text, images, and structured references. Popular use cases include knowledge extraction, trend analysis, and content development.

Use our Wikipedia Articles dataset to access a vast collection of articles across a wide range of topics, from history and science to culture and current events. This dataset offers structured data on articles, categories, and revision histories, enabling deep analysis into trends, knowledge gaps, and content development.

Tailored for researchers, data scientists, and content strategists, this dataset allows for in-depth exploration of article evolution, topic popularity, and interlinking patterns. Whether you are studying public knowledge trends, performing sentiment analysis, or developing content strategies, the Wikipedia Articles dataset provides a rich resource to understand how information is shared and consumed globally.

Dataset Features - url: Direct URL to the original Wikipedia article.
- title: The title or name of the Wikipedia article.
- table_of_contents: A list or structure outlining the article's sections and hierarchy.
- raw_text: Unprocessed full text content of the article.
- cataloged_text: Cleaned and structured version of the article’s content, optimized for analysis.
- images: Links or data on images embedded in the article.
- see_also: Related articles linked under the “See Also” section.
- references: Sources cited in the article for credibility.
- external_links: Links to external websites or resources mentioned in the article.
- categories: Tags or groupings classifying the article by topic or domain.
- timestamp: Last edit date or revision time of the article snapshot.

Distribution - Data Volume: 11 Columns and 2.19 M Rows
- Format: CSV

Usage This dataset supports a wide range of applications: - Knowledge Extraction: Identify key entities, relationships, or events from Wikipedia content.
- Content Strategy & SEO: Discover trending topics and content gaps.
- Machine Learning: Train NLP models (e.g., summarisation, classification, QA systems).
- Historical Trend Analysis: Study how public interest in topics changes over time.
- Link Graph Modeling: Understand how information is interconnected.

Coverage - Geographic Coverage: Global (multi-language Wikipedia versions also available)
- Time Range: Continuous updates; snapshots available from early 2000s to present.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It - Data Scientists: For training or testing NLP and information retrieval systems.
- Researchers: For computational linguistics, social science, or digital humanities.
- Businesses: To enhance AI-powered content tools or customer insight platforms.
- Educators/Students: For building projects, conducting research, or studying knowledge systems.

Suggested Dataset Names 1. Wikipedia Corpus+
2. Wikipedia Stream Dataset
3. Wikipedia Knowledge Bank
4. Open Wikipedia Dataset

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 283 new records are added each month. Approximately 1.12M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

Regional Price Parities: All Items for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) was 97.36000 Index in January of 2023, according to the United States Federal Reserve. Historically, Regional Price Parities: All Items for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) reached a record high of 101.12600 in January of 2011 and a record low of 96.18400 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Regional Price Parities: All Items for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) - last updated from the United States Federal Reserve on June of 2025.

WikiEvents is a knowledge graph based dataset for NLP and event-related machine learning tasks.

This dataset includes RDF data in JSON-LD about events between January 2020 and December 2022. It was extracted from the Wikipedia Current events portal, Wikidata, OpenStreetMaps Nominatim and Falcon 2.0. The extractor is available on GitHub under semantic-systems/current-events-to-kg.

The RDF data for each month is split onto four graph modules each:

• The base graph module contains events, event summaries with references from named entities to Wikipedia articles.
• The ohg graph module with all one-hop graphs (ohg) around the referencend Wikidata entities.
• The osm graph module which contains spartial data from OpenStreetMap (OSM).
• The raw graph module containing the raw HTML objects of events and article infoboxes.

This repository additionally includes two JSON files with training samples used for entity linking and event-related location extraction. They were created using queries to the WikiEvents dataset uploaded into this repository.

Crude Oil fell to 67.52 USD/Bbl on July 18, 2025, down 0.02% from the previous day. Over the past month, Crude Oil's price has fallen 8.55%, and is down 14.13% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on July of 2025.

This collection contains the dataset used for the paper "Extending OMPT to support Grain Graphs", by Peder Voldnes Langdal, Magnus Jahre, and Ananya Muddukrishna.The dataset is created by processing the textual outputs of schedbench, taskbench, BOTS, and SPEC OMP2012, and writing the aggregated data to the CSV-files provided here.The abstract of the paper is included below:The upcoming profiling API standard OMPT can describe almost all profiling events required to construct grain graphs, a recent visualization that simplifies OpenMP performance analysis. We propose OMPT extensions that provide the missing descriptions of task creation and parallel for-loop chunk scheduling events, making OMPT a sufficient, standard source for grain graphs. Our extensions adhere to OMPT design objectives and incur up to 2% overhead for BOTS and SPEC OMP2012 programs. Although motivated by grain graphs, the events described by the extensions are general and can enable cost-effective, precise measurements in other profiling tools as well.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

According to a global survey conducted in February 2024, almost 40 percent of Facebook users paid attention to news from mainstram news outlets and mainstream journalists on the social network. Additionally, 39 percent reported paying attention to personalities, such as celebrities and influencers. Around one in four Facebook users paid attention to politicians and politican activists on the network.

Data sets used for bipartite graphs network analysis.

Core consumer prices in the United States increased 2.90 percent in June of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

IntroductionOver the years, many approaches have been proposed to build ancestral recombination graphs (ARGs), graphs used to represent the genetic relationship between individuals. Among these methods, many rely on the assumption that the most likely graph is among those with the fewest recombination events. In this paper, we propose a new approach to build maximum parsimony ARGs: Reinforcement Learning (RL).MethodsWe exploit the similarities between finding the shortest path between a set of genetic sequences and their most recent common ancestor and finding the shortest path between the entrance and exit of a maze, a classic RL problem. In the maze problem, the learner, called the agent, must learn the directions to take in order to escape as quickly as possible, whereas in our problem, the agent must learn the actions to take between coalescence, mutation, and recombination in order to reach the most recent common ancestor as quickly as possible.ResultsOur results show that RL can be used to build ARGs with as few recombination events as those built with a heuristic algorithm optimized to build minimal ARGs, and sometimes even fewer. Moreover, our method allows to build a distribution of ARGs with few recombination events for a given sample, and can also generalize learning to new samples not used during the learning process.DiscussionRL is a promising and innovative approach to build ARGs. By learning to construct ARGs just from the data, our method differs from conventional methods that rely on heuristic rules or complex theoretical models.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.