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

              Social media: trust and consumption

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

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

Estimates of total businesses broken down by industry (2, 3, 4 digit SIC 2007 codes and industry section). Workplace data units from Annual Business Inquiry (ABI) for London and Great Britain. Data rounded to the nearest 100. Percentages calculated on unrounded data. An extract compiled from the Inter Departmental Business Register (IDBR) recording the number of local units that were live at a reference date in March. Estimates can be broken down by employment size band, detailed industry (5 digit SIC2007) and legal status. Available from country down to mid layer super output area and Scottish intermediate zones. A local unit is an individual site (for example a factory or shop) associated with an enterprise. It can also be referred to as a workplace. Industry is broken down using SIC 2007 codes. Read more about SIC here http://www.statistics.gov.uk/methods_quality/sic/downloads/SIC2007explanatorynotes.pdf The ABI is a business survey which collects both employment and financial information. Only employment information for the location of an employees workplace is available from Nomis The ABI is based on a sample of approximately 78,000 businesses and is used to provide an estimate of the number of employees. The difference between the estimate and its true value is known as the sampling error. The actual sampling error for any estimate is unknown but we can estimate, from the sample, a typical error, known as the standard error. This provides a means of assessing the precision of the estimate; the lower the standard error, the more confident we can be the estimate is close to the true value. https://www.nomisweb.co.uk/articles/showArticle.asp?title=Information&article=news/071212_abi-stderrors.htm This dataset excludes farm based agriculture data contained in SIC class 0100. Relevant link: https://www.nomisweb.co.uk/Default.asp

Estimates of total businesses broken down by industry (2, 3, 4 digit SIC 2007 codes and industry section). Workplace data units from Annual Business Inquiry (ABI) for London and Great Britain.

Data rounded to the nearest 100. Percentages calculated on unrounded data

Industry is broken down using SIC 2007 codes. Read more about SIC here http://www.statistics.gov.uk/methods_quality/sic/downloads/SIC2007explanatorynotes.pdf
The ABI is a business survey which collects both employment and financial information. Only employment information for the location of an employees workplace is available from Nomis
The ABI is based on a sample of approximately 78,000 businesses and is used to provide an estimate of the number of employees.
The difference between the estimate and its true value is known as the sampling error. The actual sampling error for any estimate is unknown but we can estimate, from the sample, a typical error, known as the standard error. This provides a means of assessing the precision of the estimate; the lower the standard error, the more confident we can be the estimate is close to the true value. https://www.nomisweb.co.uk/articles/showArticle.asp?title=Information&article=news/071212_abi-stderrors.htm

This dataset excludes farm based agriculture data contained in SIC class 0100.

Relevant link: https://www.nomisweb.co.uk/Default.asp

Women in Headlines: Bias

Investigating Gendered Language, Temporal Trends, and Themes

By Amber Thomas [source]

About this dataset

This dataset contains all of the data used in the Pudding essay When Women Make Headlines published in January 2022. This dataset was created to analyze gendered language, bias and language themes in news headlines from across the world. It contains headlines from top50 news publications and news agencies from four major countries - USA, UK, India and South Africa - as published by SimilarWeb (as of 2021-06-06).

To collect this data we used RapidAPI's google news API to query headlines containing one or more of keywords selected based on existing research done by Huimin Xu & team and The Swaddle team. We analyzed words used in headlines manually curating two dictionaries — gendered words about women (words that are explicitly gendered) and words that denote societal/behavioral stereotypes about women. To calculate bias scores, we utilized technology developed through Yasmeen Hitti & team’s research on gender bias text analysis. To categorize words used into themes (violence/crime, empowerment, race/ethnicity/identity etc), we manually curated four dictionaries utilizing Natural Language Processing packages for Python like spacy & nltk for our analysis. Plus, inverting polarity scores with vaderSentiment algorithm helped us shed light on differences between women-centered/non-women centered polarity levels as well as differences between global polarity baselines of each country's most visited publications & news agencies according to SimilarWeb 2020 statistics..

This dataset enables journalists, researchers and educators researching issues related to gender equity within media outlets around the world further insights into potential disparities with just a few lines of code! Any discoveries made by using this data should provide valuable support for evidence-based argumentation . Let us advocate for greater awareness towards female representation better quality coverage!

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How to use the dataset

This dataset provides a comprehensive look at the portrayal of women in headlines from 2010-2020. Using this dataset, researchers and data scientists can explore a range of topics including language used to describe women, bias associated with different topics or publications, and temporal patterns in headlines about women over time.

To use this dataset effectively, it is helpful to understand the structure of the data. The columns include headline_no_site (the text of the headline without any information about which publication it is from), time (the date and time that the article was published), country (the country where it was published), bias score (calculated using Gender Bias Taxonomy V1.0) and year (the year that the article was published).

By exploring these columns individually or combining them into groups such as by publication or by topic, there are many ways to make meaningful discoveries using this data set. For example, one could explore if certain news outlets employ more gender-biased language when writing about female subjects than other outlets or investigate whether female-centric stories have higher/lower bias scores than average for a particular topic across multiple countries over time. This type of analysis helps researchers to gain insight into how our culture's dialogue has evolved over recent years as relates to women in media coverage worldwide

Research Ideas

• A comparative, cross-country study of the usage of gendered language and the prevalence of gender bias in headlines to better understand regional differences.
• Creating an interactive visualization showing the evolution of headline bias scores over time with respect to a certain topic or population group (such as women).
• Analyzing how different themes are covered in headlines featuring women compared to those without, such as crime or violence versus empowerment or race and ethnicity, to see if there’s any difference in how they are portrayed by the media

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: headlines_reduced_temporal.csv | Column name | Description | |:---------------------|:-------------------------------------------------------------------------------------...

Employment (workplace) by industry from the Business register and employment survey (BRES). This data excludes self-employed but includes proprietors
Employment = employees + working proprietors. Working Proprietors are sole traders, sole proprietors, partners and directors. This does not apply to registered charities.
Numbers have all been rounded to the nearest 100
Before the BRES first existed in 2009, the ABI collected employment data by industry. The two surveys are not directly comparable. The BRES is a business survey which collects both employment and financial information. Only employment information for the location of an employees workplace is available from Nomis
The BRES is based on a sample of approximately 80,000 businesses and is used to provide an estimate of the number of employees.
The difference between the estimate and its true value is known as the sampling error. The actual sampling error for any estimate is unknown but we can estimate, from the sample, a typical error, known as the standard error. This provides a means of assessing the precision of the estimate; the lower the standard error, the more confident we can be the estimate is close to the true value. NOMIS website article

This dataset excludes farm based agriculture data contained in SIC class 0100.

Data and charts accompanying the 'Business Register Employment Survey 2010: London' publication

The ABI was replaced by the Business Register and Employment Survey (BRES) from 2009 onwards, therefore this dataset will no longer be updated.

More on ONS website

The data from my thesis. This data was collected using the Lifeguide Software and exported onto SPSS following data collection. The data was collected from young people aged 11-18 years old to explore the impact of different types of social media use.

Mental Health Services for Children

Monthly Statistics from England

By data.world's Admin [source]

About this dataset

This dataset provides insight into the mental health services available to children and young people in England. The data includes all primary and secondary levels of care, as well as breakdowns by age group. Information is provided on the number of people in contact with mental health services; open ward stays; open referrals; referrals starting in reporting period; attended contacts; indirect activity; discharged from referral; missed care contacts by DNA reasons and more. With these statistics, analysts may be able to better understand the scope of mental health service usage across different age groups in England and make valuable conclusions about best practices for helping children & young people receive proper care

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How to use the dataset

This guide provides information on how to use this dataset effectively.

Understanding the Columns:

Each row represents data from a specific month within a reporting period. The first thing to do is to find out what each column represents - this is explained by their titles and descriptions included at the beginning of this dataset. Note that there are primary level columns (e.g., Reporting Period, Breakdown) which provide overall context while secondary level columns (e.g., CYP01 People in contact with children and young peoples' mentally health service…) provide more detail on specific indicators of interest related to that primary level column value pair (i.e., Reporting Period X).

Exploring Data Variables:

The next step is exploring which data variables could potentially be helpful when analyzing initiatives/programs related to mental health care for children & youth in England or developing policies related to them – look through all columns included here for ones you think would be most helpful such as ‘CYP21 – Open ward stays...’ or ‘MHS07a - People with an open hospital spell…’ and note down those that have been considered necessary/relevant based on your particular situation/needs before further analyzing using software packages like Excel or SPSS etc..

Analyzing Data Values:

Now comes the time for analyzing individual values provided under each respective column – take one single numerical data element such as ‘CYP02 – People… CPA end RP’ & run through it all looking at trends over time, averages across different sections by performing calculations via software packages available like tables provided above based upon sorted hierarchies needed.. Then you can then start looking into making meaningful correlations between different pieces of information given herein by cross-referencing contexts against each other resulting if any noticeable patterns found significant enough will make informative decisions towards policy implementations & program improvement opportunities both directly concerned

Research Ideas

• Using this dataset to identify key trends in mental health services usage among children and young people in England, such as the number of open ward stays and referrals received.
• Using the information to develop targeted solutions on areas of need identified from the data by geographical area or age group, i.e creating campaigns or programs specifically targeting specific groups at a higher risk of experiencing mental health difficulties or engaging with specialist services.
• Tracking how well these initiatives are working over time by monitoring relevant metrics such as attendance at appointments, open referrals etc to evaluate their effectiveness in improving access and engagement with mental health services for those most in need

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - ...

The Adults' Media Use and Attitudes report is published as part of Ofcom's media literacy duties. It provides data on adults' media use and attitudes across TV, radio, games, mobile and the internet, with a particular focus on online use and attitudes. The research underpinning this report was first conducted in 2005. The 2016 report focuses on the current wave of research which was conducted in autumn 2015, and any key changes since 2014. Media literacy enables people to have the skills, knowledge and understanding they need to make full use of the opportunities presented both by traditional and by new communications services. Media literacy also helps people to manage content and communications, and protect themselves and their families from the potential risks associated with using these services.

Supplemental information files for article News consumption and immigration attitudes: a mixed methods approach

Existing research has shown that the media can influence public attitudes to immigration. While existing research provides insight into both quantitative and qualitative patterns of media coverage of immigration, research that links such coverage with audience attitudes is almost exclusively quantitative, often focused on the west, and are often single-country studies. We argue that the adoption of a mixed-methods approach to audiences of immigration news, combined with a comparative design and a focus on Eastern Europe – a region scoring lowest in the world in terms of migrant acceptance – can bring significant advances to knowledge in this area, leading to a more rounded understanding of how media come to shape immigration attitudes. To demonstrate this, we draw on a comparative, mixed-methods data set comprising representative population surveys (N=4,092), an expert survey (N=60), and qualitative interviews (N=120) conducted in four Eastern European countries. In contrast to existing research on Western Europe, we found significant variation in the links between attitudes to immigration and use of Public Service Media (PSM), with PSM consumption linked with more negative attitudes to immigration in some countries, and with more positive attitudes in others. Second, our results confirm that different attitudes to immigration are embedded in different qualitative understandings of immigration: while participants with more positive attitudes often adopted a more inclusive understanding of immigration, those with more negative attitudes adopted a narrower understanding. Third, we demonstrated the importance of family and acquaintances as trusted sources of information.

Monthly usage figures for online resources including databases and e-book platforms when available, for January 2005 to present. Additional information Blank means no data available In 2020, all library buildings closed from 19 March included due to the coronavirus outbreak. Resources included : description {minimum dates of subscription} What the figure is 19th Century British Library Newspapers : digital newspaper archive {May 2007 - present} Number of sessions Access to Research : online journals {April 2014 - present} Number of pages viewed Ancestry : family history {October 2008 - present} Number of sessions until May 2015; number of content pages viewed from June 2015 Britannica Online : encyclopedia {January 2005? - present} Number of searches conducted, until June 2014; number of sessions from July 2014 British Standards {March 2005 - April 2017; November 2017 - present} Number of content pages viewed British Way of Life : information to help asylum seekers, refugees and migrants in getting settled in the UK {October 2016 - January 2023} Number of sessions - subscription ceased January 2023 Citizens Advice Notes : UK law made understandable {March 2007 - March 2016} Number of pages viewed COBRA : business information fact sheets and business sector profiles {October 2005 - present} Number of pages viewed Corporate researcher / Market IQ : company information database {January 2008 - 2015} Number of "reports viewed" EISODOS : information for foreigners coming to live in the UK {October 2008 - October 2013} Information on meaning of figure lost Enquire : "ask a librarian" online chat service {2005 - March 2016} Number of chats started by users in the Newcastle area Find my past : family history {April 2011 - present} Number of sessions (or so we seem to remember when we had access to usage figures) Go Citizen : replaces Life in Great Britain, citizenship test preparing for UK citizenship. {September 2023 - present} Number of tests taken IBISWorld : market research {January 2017 - present} Number of pages viewed Key Note : company information and market research {April 2011 - October 2018} Number of reports viewed Kompass : business information {2006 - July 2011} Information on meaning of figure lost Know UK : current reference information {January 2007 - June 2011} Information on meaning of figure lost Life in Great Britain : self-learn course to prepare for the Life in the UK citizenship test {January 2010 - January 2023} Number of sessions - subscription ceased January 2023 Local Data Online : business (retail sector) information {November 2013 - July 2015?} Number of queries per month. No longer receive stats on this as of July 2024. Mint UK & Mint Global : company information databases {March 2014 - 2015} Information on meaning of figure lost Mintel : market reports {2006? - April 2010; June 2013 - present} Number of reports viewed Newsstand : online newspapers {January 2011 - March 2014} Information on meaning of figure lost Onesource / Avention : company information database (changed name over the years) {March 2012 - October 2013; July 2015 - present} Number of searches conducted - Subscription ceased June 2024 News UK : newspaper articles {January 2007 - October 2010?} Information on meaning of figure lost Oxford English Dictionary {May 2006 - present} Number of sessions Oxford Art Online {March 2006 - present} Number of sessions Oxford Dictionaries {February 2015 - present} Number of sessions Oxford Dictionary of National Biography {January 2006 - present} Number of sessions Oxford Music Online {March 2006 - present} Number of sessions Oxford Reference Online {March 2006 - present} Number of sessions Safari Select : online books (to read online, as opposed to the e-books you can download and read offline) {May 2009 - March 2014} Number of books viewed Times Digital Archive : digitised newspapers {January 2005 - present} Number of sessions Theory Test Pro : practice questions for the driving theory test {August 2010 - present} Number of sessions Transparent language online / Byki : language courses {January 2011 - November 2012} Number of courses accessed Universal Skills : learn basic computer skills and how to use Universal Job Match {November 2014 - present} Number of users Newcastle Library App (devices) : number of devices the app is on {2013 - present} Newcastle Library App (launches) : number of times the app has been used {2013 - present} Bibliotheca Cloud Library : e-books and e-audiobooks {February 2016 - March 2018} Number of items borrowed Bolinda : e-audiobooks collection {2012 - February 2016} Number of items borrowed (figures only from April 2015) Bolinda BorrowBox e-books {February 2018 - present} Number of items borrowed Bolinda BorrowBox e-audiobooks {February 2018 - present} Number of items borrowed ComicsPlus : e-comic books {March 2017} Number of items borrowed - no longer record this, not sure when subscription ceased OneClick / RB Digital (e-audiobooks) : e-audiobooks collection (became RB Digital in... 2017?) {May 2015} Number of items borrowed - no longer record this, not sure when subscription ceased Overdrive (e-audiobooks) {2011 - May 2016} Number of items borrowed (figures only from April 2015) - subscription ceased January 2023016} Number of items borrowed (figures only from April 2015) - subscription ceased March 2023 Public Library Online : e-books collection {April 2016 - February 2018} Number of items borrowed Zinio / RB Digital (magazines) : digital magazines (the Zinio service became integrated with the other RB Digital content in 2017) {May 2015 - present} Number of magazines downloaded (figures only from January 2016)

"This dataset includes consumer-submitted reviews from over 479 companies, covering both product- and service-based businesses. It’s built to support CX, AI, and analytics teams seeking structured insight into what real customers say, feel, and expect — across the Social Media and Networking industry.

Each review includes:

• Authentic customer reviews (text, rating, pros and cons)
• Labeled sentiment and tone (positive, neutral, negative)
• Service context across industries: purchase, delivery, support, return, usage
• Industry and company filters (fully customizable per buyer request)
• Optional metadata: platform, review length, timestamp, geo-location

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

• Track public perception trends across specific brands or verticals
• Segment sentiment insights by industry, region, or company
• Power NLP pipelines that require diverse tone, emotion, and domain specificity
• Build dashboards or LLM prompts grounded in real user language
• Train review summarization, classification, or escalation engines

This dataset offers flexibility for custom delivery-by industry, domain, or company, making it ideal for teams needing scalable consumer voice data tailored to specific strategic goals."

Employment in the United Kingdom decreased by 22 in September of 2025. This dataset provides - United Kingdom Employment Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, Google ranked as the most valuable media and entertainment brand worldwide, with a brand value of 683 billion U.S. dollars. Facebook ranked second, valued at around 167 billion dollars. Part of the Tencent Group, WeChat and v.qq.com (Tencent Video) had a brand value of 56 billion and 17.5 billion dollars, respectively.

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, 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 here available Twitter data (tweet IDs) was originally collected live, using the official Twitter REST API search endpoint, following their terms of service and developer guidelines.Tweet IDs are usually 18-19 long numeric IDs, and these tweet IDs can be rehydrated, which is a simple process involving sending requests to the official Twitter API /statuses/lookup endpoint with tweet IDs uniquely identifying a tweet, and Twitter responding appropriately with the tweet and metadata. This helps ensure that if a user has set their account to private or deleted their tweet, effectively withdrawing consent, their tweet would not be available for further analysis.There are several convenient utilities that researchers can use to rehydrate Twitter data from tweet IDs. One such tool is the DocNow Hydrator, available at https://github.com/DocNow/hydrator, as well as a useful detailed guide on how to go about it, available at https://programminghistorian.org/en/lessons/beginners-guide-to-twitter-data#hydrating.