The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027. What is Big data? Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big data analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

View details of Granule Import Data of Spread Big Trade Limited Supplier to US with product description, price, date, quantity, major us ports, countries and more.

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Context

As we all know, Fake-News has become the centre of attraction worldwide because of its hazardous impact on our society. One of the recent example is spread of Fake-news related to Covid-19 cure, precautions, and symptoms and you must be understood by now, how dangerous this bogus information could be. Distorted piece of information propagated at the times of election for achieving political agenda is not hidden from anyone.

Fake news is quickly becoming an epidemic, and it alarms and angers me how often and how rapidly totally fabricated stories circulate. Why? In the first place, the deceptive effect: the fact that if a lie is repeated enough times, you’ll begin to believe it’s true.

You understand by now that fake news and other types of false information can take on various appearances. They can likewise have significant effects, because information shapes our world view: we make important decisions based on information. We form an idea about people or a situation by obtaining information. So if the information we saw on the Web is invented, false, exaggerated or distorted, we won’t make good decisions.

Hence, Its in dire need to do something about it and It's a Big Data problem, where data scientist can contribute from their end to fight against Fake-News.

Content

Although, fighting against fake-News is a big data problem but I have created this small dataset having approx. 10,000 piece of news article and meta-data scraped through approx. 600 web-pages of Politifact website to analyse it using data science skills and get some insights of how can we stop spread of misinformation at broader aspect and what approach will give us better accuracy to achieve the same.

This dataset is having 6 attributes among which News_Headline is the most important to us in order to classify news as FALSE or TRUE. As you notice the Label attribute clearly, there are 6 classes specified in it. So, it's totally up-to you whether you want to use my dataset for multi-class classification or convert these class labels into FALSE or TRUE and then, perform binary classification. Although, for your convenience, I will write a notebook on how to convert this dataset from multi-class to binary-class. To deal with the text data, you need to have good hands on practice on NLP & Data-Mining concepts.

• News_Headline - contains piece of information that has to be analysed.
• Link_Of_News - contains url of News Headlines specified in very first column.
• Source - this column contains author names who has posted the information on facebook, instagram, twitter or any other social-media platform.
• Stated_On - This column contains date when the information is posted by the authors on different social-media platforms.
• Date - This column contains date when this piece of information is analysed by politifact team of fact-checkers in order to labelize as FAKE or REAL.
• Label - This column contains 5 class labels : True, Mostly-True, Half-True, Barely-True, False, Pants on Fire.

So, you can either perform multi-class classification on it or convert Mostly-True, Half-True, Barely-True as True and drop Pants on Fire and perform Binary-class classification.

Acknowledgements

• A very Big thanks to fact-checking team of Politifact.com website as they provide with correct labels by working hard manually. So that we data science people can take advantage to train our models on such labels and make better models. These are some research papers that will help you to get start with the project and clear your fundamentals.

• "https://journals.sagepub.com/doi/full/10.1177/2053951719843310">Big Data and quality data for fake news and misinformation detection by Fatemeh Torabi Asr, Maite Taboada

• "https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/pra2.2015.145052010082">Automatic deception detection: Methods for finding fake news by Nadia K. Conroy Victoria L. Rubin Yimin Chen

Inspiration

• I want to see which approach can solve this problem of combating Fake-News with greater accuracy.

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According to Cognitive Market Research, the global Data Center Fabric Market size is USD 2.31 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 28.89% from 2024 to 2031. Market Dynamics of Data Center Fabric Market

Key Drivers for Data Center Fabric Market

Cloud Computing and Data Analytics- The surge in cloud computing and big data analytics is a primary driver for the Data Center Fabric Market. As organizations increasingly migrate to cloud-based services and harness big data for insights, there is a growing demand for high-speed, scalable, and efficient data center networks. Data center fabrics provide the necessary infrastructure to support the vast amounts of data generated and processed, ensuring seamless connectivity, low latency, and high throughput. This driver is critical as it addresses the core needs of modern enterprises to manage and analyze data effectively, supporting innovation and competitive advantage.
The cloud computing demand and virtualization technology advancements are anticipated to drive the data center fabric market's expansion in the years ahead.
Increasing Need for Cloud and Edge Computing is driving the market to grow.

The rising trend of adoption of cloud and edge computing is boosting the Data Center Fabric Market Industry. Cloud computing offers enterprises on-demand access to computing resources without having to purchase and maintain their own IT equipment. Edge computing moves computing resources closer to the end-user, lowering latency and enhancing performance. Both these trends are propelling the demand for low-latency, high-speed data center networks, which in turn is propelling the data center fabric market growth. Also, the increasing use of artificial intelligence (AI) and machine learning (ML) is propelling the demand for data center fabrics. AI and ML need huge data to train and run, and this data tends to be housed in data centers. As the volume of data consumed for AI and ML increases, the need for data center fabrics for connecting and servicing this data increases. Lastly, the expanding penetration of 5G networks also fuels the development of the market for data center fabrics.5G networks require low-latency, high-speed connections, and data center fabrics can deliver those. As 5G networks spread, so will the need for data center fabrics to enable them.

Key Restraints for Data Center Fabric Market

The complexity of integrating data center fabric solutions into existing infrastructure, coupled with the significant upfront investment required for implementation, limits the market growth.
High initial investment costs for deployment and infrastructure upgrades impact the market growth.

Key Opportunity of Market.

Advancements in cloud computing technologies can be an Opportunity.

The growing use of cloud computing services by companies, as they provide flexibility, scalability, and cost-effective solutions, is giving a boost to the market growth. Along with this, the extensive spread of cloud-based applications and services that result in greater dependence on data centers, thus driving the demand for data center fabrics, is promoting the market growth. As a result of this, data fabrics form extremely scalable and responsive cloud computing platforms. Additionally, they provide quicker communication between storage devices and servers, which is indispensable for cloud-based services demanding rapid data access and processing. What's more, the increasing movement to the cloud by enterprises and the growth in cloud services, increasing the demand for advanced data center fabrics, is fueling the market.

Introduction of the Data Center Fabric Market

The Data Center Fabric Market is poised for dynamic growth by increasing demand for scalable, high-performance data center networks to support emerging technologies like cloud computing, big data analytics, and artificial intelligence. The proliferation of data-intensive applications and the need for rapid data processing capabilities are fueling the adoption of data center fabrics. Moreover, the shift towards virtualization and software-defined networking is driving the deployment of flexible and agile network architectures. As organizations strive to optimize their data center operations for efficiency and scalability, the Data Center Fabric Market is expected to witness significant growth, supported by advancements in networ...

In a survey conducted in May 2025, journalism was rated the most positively by U.S. adults, with 54 percent describing it as very or somewhat favorable. Social media followed with 49 percent favorable, though a notable share of respondents also held negative views. The news media and the press were rated less positively, at 47 and 46 percent, respectively. Overall, the findings suggest stronger confidence in journalism compared to other media institutions.

According to a survey conducted in May 2025, 56 percent of adults in the United States said they actively seek out news, while 35 percent reported that news usually comes to them. A smaller share were unsure about their news consumption habits.

Dataset associated with "The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain"

In the anat folder, three additional files beyond the T1w MPRAGE can be found:

1. An inversion recovery T1 shuffled EPI, named with acq-epi and a suffix _inplaneT1.
2. A point spread function (PSF) mapping scan, named with acq-psf and a suffix _T2starw.
3. A larger field of view dataset used to aid registration, named with acq-epi and a suffix _T2starw.

The larger field of view dataset was used in the work that resulted in our publication, but the T1 shuffled EPI and the PSF were not. They are added here though in the hope that they may be for other purposes useful.

This is data collected throughout 2017 and 2019 which contains one of a selection of hashtags related to Brexit. The data only contains the tweet id's. The data was collected in order to examine what made people forward facts and counter-facts. The tweets were examined to see how often they had been retweeted and on what time scale. We looked at the content of the tweets and analyzed the tweet metadata. We found that who you are is often more important than what you say with tweets from celebrities and verified uses being forward more often. We found that tweets with images were much more likely to be retweeted. We discovered with content related forwards the resonance of an issue was not determined by how many times something was said but by how often something was retweeted and over what period of time.

This collaborative project between the Neuropolitics Research Lab (NRlabs), at the University of Edinburgh and Full Fact, the UK's independent fact-checking organization, employs neuroscientific, psychological and behavioural insights to help us to understand what makes Brexit-related claims spread on digital platforms. Using cutting edge scientific techniques in big data analysis this project offers new insights into how citizens' expectations on Brexit and its consequences are shaped in an increasingly digital world. It will inform organisations on how to communicate what is often dry and complex information related to Brexit in a credible, trustworthy and memorable way using digital communications. These insights will be essential for the strategic management, implementation and public communication of the Article 50 process for the UK's withdrawal from the EU. The question of what constitutes a fact (or an alternative fact) has perhaps never been more salient in public debate. The thirst for 'facts' during the Brexit referendum campaign was a key feature of public debate as was the question of whose facts count. The role of experts in the delivery of factual information came under close scrutiny and became a substantive feature of campaign dialogues. The question of trust and authority in information transmission has been under serious challenge. Citizens' expectations of Brexit and its consequences are, at least in part, shaped by their evaluation of the facts - but how do they decide what is a trustworthy fact? What factors lead them to imbue some sources of information with greater authority than others and under what circumstances do they choose to engage with, share or champion certain 'facts'? How does the context in which 'facts' are disseminated shape the expectations of the citizens on Brexit? Digital technology and online communication platforms such as Twitter and Facebook, play an increasingly important role in the public communication of both information and misinformation. To date, however, we have little information on how 'facts' transmitted in these digital platforms are internalized by recipients and on how this information impacts on citizens' expectations. We investigate how membership of a specific social media bubble impacts on the evaluation of the information received; how the status of the sender or even the content of the communication (whether it contains an image or a web link) matters; and how the nature of the information received, confirmatory or challenging of previous knowledge, impacts on fact transmission to different publics. This project builds on the extensive engagement of two research teams on Brexit-related research and with the UK in a Changing Europe team. Both teams are engaged at the highest level in stakeholder engagement and the project is built on a co-production model, ensuring that the issues addressed are of direct interest to those most likely to utilise the insights developed directly in their daily work. The project is designed in close collaboration with stakeholders to ensure that it can adapt swiftly to maintain relevance in the fast-moving Brexit environment. The project has access to a unique social media data-base of over 40 million tweets that NRlabs has collected on the Brexit debate since August 2015; the cutting edge skills and facilities for conducting experimental research at NRlabs; and ensures daily policy relevance through Full Fact's engagement, nationally and internationally, in the fact checking environment. The contribution of this project addresses the very heart of the mission of the UK in a Changing Europe programme - to be the authoritative source for independent research on UK-EU relations, underpinned by scientific excellence and generating and communicating innovative research with real world impact.

E-commerce companies sell various goods and associated services through online portals, either on websites, mobile apps or integrated into social media platforms. Internet access across Europe continues to accelerate, with the vast majority of countries boasting usage rates of over 80% of the population. The spread of fast broadband and mobile data has enabled rising numbers of Europeans to engage in e-shopping. Over the five years through 2025, e-commerce revenue is slated to climb at a compound annual rate of 4% to reach €352.5 billion. E-tailers benefit from lower overhead costs than bricks-and-mortar stores, enabling them to offer highly competitive prices and draw sales away from traditionally popular establishments like department stores. E-tailers have taken off by leveraging these cost advantages to appeal to an increasingly price-conscious consumer base. The expansion of value-added services like buy now, pay later and fast, flexible delivery options have contributed to strong industry growth. However, the industry hasn’t been immune to recent cos-of-living pressures; sky-high inflation across much of Europe severely dented Europeans’ spending power, with drops in sales volumes affecting many online stores in 2023. Despite this, revenue continues on an upwards trajectory as inflation outweighs the drop in volume sales, contributing to forecast revenue growth of 3.9% in 2025. Looking forwards, rising internet penetration will continue to provide a growing market for e-tailers, driving revenue upwards at a projected compound annual rate of 6.3% over the five years through 2030 to reach €478.9 billion. E-tailers will continue to adapt their business practices and product selections to reflect the ever-growing level of environmental awareness. Delivery fleets will become fully electrified for many companies, while increasingly stringent waste regulations will force companies to adopt biodegradable or recyclable packaging in the coming years. Still, online retailers must innovate to compete with rival Asian companies like Temu as these competitors increasingly penetrate European markets. The integration of Gen AI and data analytics will transform business operations, making them more efficient and helping to lower wage costs, supporting profitability.

In May 2025, a survey asked U.S. adults how they feel while consuming news. The results indicate that a majority feel informed, with 53 percent saying that news generally makes them feel this way. At the same time, 43 percent reported feeling angry, and 32 percent said they feel depressed when consuming news. In contrast, only 16 percent described feeling hopeful. These findings highlight that while staying informed is a major benefit of news consumption, negative emotional reactions—such as anger and depression—are also very common among Americans.

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Global network characteristics. Each row corresponds to a specific global measure. Each column corresponds to the market, the FGD or the combined (market+FGD) network.

A 2025 survey found that around one in four adults in the United States actively avoided news related to sports, followed by entertainment (18 percent) and lifestyle (17 percent). In contrast, health was the least avoided news topic, with just four percent of respondents saying they ignored it.

Safety Performance Functions Analytics Market Outlook

As per our latest research, the global Safety Performance Functions Analytics market size reached USD 1.42 billion in 2024, with a robust year-on-year growth trajectory. The market is exhibiting a compound annual growth rate (CAGR) of 12.3% from 2025 to 2033, projecting the market to achieve a value of USD 4.05 billion by 2033. This remarkable growth is driven by the increasing adoption of advanced analytics in transportation safety, the proliferation of smart city initiatives, and the urgent need for data-driven decision-making to reduce roadway accidents and enhance infrastructure planning.

One of the primary growth factors propelling the Safety Performance Functions Analytics market is the rising emphasis on roadway safety and accident prevention across the globe. Governments and transportation authorities are increasingly relying on sophisticated analytics solutions to identify high-risk locations, predict accident hotspots, and implement targeted interventions. The integration of real-time data from IoT sensors, traffic cameras, and connected vehicles has significantly improved the accuracy and predictive power of safety performance functions, enabling authorities to take proactive measures and optimize resource allocation. This data-driven approach not only enhances public safety but also reduces economic losses associated with traffic accidents, thus fueling the demand for advanced analytics solutions in this domain.

Another key driver is the rapid digital transformation within the transportation sector, spurred by advancements in machine learning, artificial intelligence, and big data analytics. These technologies are enabling the development of more sophisticated safety performance models that can analyze vast amounts of heterogeneous data, including weather conditions, traffic flow, driver behavior, and infrastructure characteristics. The growing availability of cloud-based analytics platforms has further democratized access to these tools, allowing even smaller municipalities and consulting firms to leverage state-of-the-art safety analytics without significant upfront investments in IT infrastructure. This democratization is expanding the addressable market and encouraging innovation in the field.

Additionally, the global push towards smart cities and sustainable urban mobility is creating a fertile environment for the adoption of Safety Performance Functions Analytics. Urban planners and infrastructure developers are increasingly incorporating safety analytics into their planning processes to design safer roads, optimize traffic management, and achieve Vision Zero goals. The availability of granular, location-specific safety insights is enabling more effective policy-making and infrastructure investments, which is particularly critical in rapidly urbanizing regions. This trend is expected to further accelerate market growth, as cities worldwide prioritize safety and resilience in their transportation networks.

From a regional perspective, North America currently dominates the Safety Performance Functions Analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of stringent regulatory frameworks, early adoption of advanced analytics technologies, and substantial investments in transportation safety initiatives are key factors contributing to the region's leadership. However, Asia Pacific is anticipated to exhibit the highest CAGR during the forecast period, driven by rapid urbanization, increasing government focus on road safety, and the expansion of smart city projects in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as awareness around roadway safety and data-driven decision-making continues to spread.

Component Analysis

The Component segment of the Safety Performance Functions Analytics market is categorized into Software, Hardware, and Services, each playing a crucial role in the overall ecosystem. Software solutions constitute the largest share of the market, as they provide the analytical engines, visualization dashboards, and predictive modeling tools essential for extracting actionable insights from vast datasets. These software platforms are increasingly leveraging artificial intelligence and machine learning algorithms

Our study aims to gauge public opinion of claims made around ‘Brexit’ (i.e., Great Britain’s departure from the European Union). In this study, several such claims have been fact-checked to determine their truthfulness and accuracy. Purpose was to test the interactive effects of conclusion strength/framing, explanation structure, and information source detail availability on the efficacy of fact-checks on politically contentious claims. Further, to assess if reactions to fact-checks were moderated by political orientation and/or (mis)match between fact-check stance and preferred political outcome.

This collaborative project between the Neuropolitics Research Lab (NRlabs), at the University of Edinburgh and Full Fact, the UK's independent fact-checking organization, employs neuroscientific, psychological and behavioural insights to help us to understand what makes Brexit-related claims spread on digital platforms. Using cutting edge scientific techniques in big data analysis this project offers new insights into how citizens' expectations on Brexit and its consequences are shaped in an increasingly digital world. It will inform organisations on how to communicate what is often dry and complex information related to Brexit in a credible, trustworthy and memorable way using digital communications. These insights will be essential for the strategic management, implementation and public communication of the Article 50 process for the UK's withdrawal from the EU. The question of what constitutes a fact (or an alternative fact) has perhaps never been more salient in public debate. The thirst for 'facts' during the Brexit referendum campaign was a key feature of public debate as was the question of whose facts count. The role of experts in the delivery of factual information came under close scrutiny and became a substantive feature of campaign dialogues. The question of trust and authority in information transmission has been under serious challenge. Citizens' expectations of Brexit and its consequences are, at least in part, shaped by their evaluation of the facts - but how do they decide what is a trustworthy fact? What factors lead them to imbue some sources of information with greater authority than others and under what circumstances do they choose to engage with, share or champion certain 'facts'? How does the context in which 'facts' are disseminated shape the expectations of the citizens on Brexit?

Digital technology and online communication platforms such as Twitter and Facebook, play an increasingly important role in the public communication of both information and misinformation. To date, however, we have little information on how 'facts' transmitted in these digital platforms are internalized by recipients and on how this information impacts on citizens' expectations. We investigate how membership of a specific social media bubble impacts on the evaluation of the information received; how the status of the sender or even the content of the communication (whether it contains an image or a web link) matters; and how the nature of the information received, confirmatory or challenging of previous knowledge, impacts on fact transmission to different publics.

This project builds on the extensive engagement of two research teams on Brexit-related research and with the UK in a Changing Europe team. Both teams are engaged at the highest level in stakeholder engagement and the project is built on a co-production model, ensuring that the issues addressed are of direct interest to those most likely to utilise the insights developed directly in their daily work. The project is designed in close collaboration with stakeholders to ensure that it can adapt swiftly to maintain relevance in the fast-moving Brexit environment. The project has access to a unique social media data-base of over 40 million tweets that NRlabs has collected on the Brexit debate since August 2015; the cutting edge skills and facilities for conducting experimental research at NRlabs; and ensures daily policy relevance through Full Fact's engagement, nationally and internationally, in the fact checking environment. The contribution of this project addresses the very heart of the mission of the UK in a Changing Europe programme - to be the authoritative source for independent research on UK-EU relations, underpinned by scientific excellence and generating and communicating innovative research with real world impact.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The COVID Tracking Project was a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States. Our dataset was in use by national and local news organizations across the United States and by research projects and agencies worldwide.

In the US, health data infrastructure has always been siloed. Fifty-six states and territories maintain pipelines to collect infectious disease data, each built differently and subject to different limitations. The unique constraints of these uncoordinated state data systems, combined with an absence of federal guidance on how states should report public data, created a big problem when it came to assembling a national picture of COVID-19's spread in the US: Each state has made different choices about which metrics to report and how to define them—or has even had its hand forced by technical limitations.

Those decisions have affected both The COVID Tracking Project's data, assembled from states' public data releases, and still affect the CDC's data, which mostly comes from submissions from state and territorial health departments. And they have had real consequences for the numbers: A state's data definitions might be the difference between the state appearing to have 5% versus 20% test positivity, between labeling a COVID-19 case as active versus recovered, or between counting or not counting large numbers of COVID-19 cases and deaths at all.

Because state definitions affect the data we collect, COVID Tracking Project researchers have needed to maintain structured, detailed records on how states define all the testing and outcomes data points we capture in our API (and a few we don't). Internally, we call this constantly evolving body of knowledge "annotations." Today, we are for the first time publishing our complete collection of annotations.

The global Emotion Recognition and Analysis market is poised for significant expansion, projected to reach a market size of approximately USD 5.8 billion by 2025. Driven by a Compound Annual Growth Rate (CAGR) of around 21.5% from 2025 to 2033, this burgeoning industry is set to witness substantial value creation, estimated to reach over USD 25 billion by the end of the forecast period. This remarkable growth is fueled by a confluence of factors, with the increasing adoption of AI-powered analytics across diverse sectors standing out as a primary driver. The escalating demand for enhanced customer experience and personalized services in industries such as retail, marketing, and entertainment is a key catalyst. Furthermore, the growing application of emotion recognition in surveillance and security systems, coupled with advancements in speech and facial recognition technologies, is propelling market adoption. Emerging economies, particularly in the Asia Pacific region, are also demonstrating increasing interest and investment in these technologies, further augmenting global market potential. The market for Emotion Recognition and Analysis is characterized by a dynamic landscape of technological innovation and evolving applications. While facial and speech recognition dominate the current segmentation, newer, more sophisticated analytical methods are emerging. Key market restraints include concerns surrounding data privacy and ethical implications, which necessitate robust regulatory frameworks and responsible development practices. However, these challenges are increasingly being addressed through advancements in anonymization techniques and transparent data handling. Leading companies like Microsoft, IBM, and Affectiva are at the forefront of innovation, investing heavily in research and development to refine algorithms and expand their service offerings. The strategic importance of understanding human emotions through technology is becoming paramount for businesses seeking to gain a competitive edge, improve operational efficiency, and foster deeper connections with their target audiences. The comprehensive market study covers a wide geographical spread, with North America and Europe currently leading in adoption, while the Asia Pacific region shows immense promise for future growth. This report delves into the dynamic and rapidly evolving market for Emotion Recognition and Analysis (ERA). Spanning a study period from 2019 to 2033, with a base year of 2025 and an estimated year also of 2025, this analysis provides an in-depth look at market dynamics during the historical period (2019-2024) and forecasts critical trends for the forecast period (2025-2033). The global market for ERA is projected to reach significant figures, with an estimated market size of $5,800 million in 2025, poised for substantial growth in the coming years.