Survey data collected in Canada, 2019. n = 1539. Using, Age, Facebook use and meme understanding to determine differences between demographics in relation to Instagram use

According to a survey of internet users in Great Britain (GB) conducted between July 20 and 23, over half of respondents aged 18 to 24 reported having used Facebook in this period. The same amount of users in the age cohort reported having used TikTok, while 81 percent used YouTube in the examined time. Overall, Facebook was the most popular social media platform across the older demographics, with 72 percent of users aged between 50 and 64 reporting to having engaged with the Meta-powered social media in the examined period. The 2024 general election in the United Kingdom was held on July 4, and saw parties and politicians make ample usage of social media channels in the weeks before the country casted its vote.

According to a 2022 survey conducted in the United States, 38 percent of Generation Z respondents reported spending more than four hours on social media daily - almost double the amount of time adults of other generations spent on social media daily. Overall, one in five adults spent less than one hour per day on social media.

Gen Z and Millennials are the biggest social media users of all age groups.

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.

The results might surprise you when looking at internet users that are active on social media in each country.

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).

Dataset Details

This dataset contains a rich collection of popular slang terms and acronyms used primarily by Generation Z. It includes detailed descriptions of each term, its context of use, and practical examples that demonstrate how the slang is used in real-life conversations. The dataset is designed to capture the unique and evolving language patterns of GenZ, reflecting their communication style in digital spaces such as social media, text messaging, and online forums. Each… See the full description on the dataset page: https://huggingface.co/datasets/MLBtrio/genz-slang-dataset.

The average Twitter user spends 5.1 hours per month on the platform.

Canadian Internet use survey, Internet use, by location of use, household income quartile and age group for Canada and regions, from 2010 and 2012.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

Percentage of Internet users by selected Internet service and technology, such as; home Internet access, use of smart home devices, use of smartphones, use of social networking accounts, use or purchase of streaming services, use of government services online and online shopping.

As of January 2025, 78 percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were 75 percent of 30 to 39 year olds in the country. Overall, 66 percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach 342 million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive 91.56 percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for 45 percent of all social media visits in 2023, followed by Pinterest with 21.2 percent of visits. The global social media landscape As of April 2024, 5.07 billion people were social media users, accounting for 62.6 percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of 80.2 percent, followed by Western Europe with 78.2 percent and Eastern Asia 74.9 percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. 272014 YouTube, Instagram, and WhatsApp followed, all with two billion or more MAU.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

This Zenodo item contains the dataset of the study: Manca, S. (2020). “Bridging cultural studies and learning science: An investigation of social media use for Holocaust memory and education in the digital age".

Abstract

Along with advances in communication technology that are making new forms of historical memorialization and education available, social media are researched as valuable tools for supporting forms of digital memory and for engaging students and teachers about historical knowledge and moral education. This study aims to map the current state of Holocaust remembrance and Holocaust education and to identify main topics of research in the two areas. It adopts a mixed-method approach that combines qualitative analysis with bibliometric approaches to review publications that use social media for digital memory and history education about the Holocaust. Results based on 28 publications reveal several research topics and that, despite some common theoretical references, the two subfields mostly rely on separate conceptual backgrounds. While Holocaust remembrance is a well-established research field, there are few studies and a lack of theoretical elaboration about social media use for teaching and learning about the Holocaust.

Older adults have recently begun to adopt social media in increasing numbers. Even so, little is known about the factors influencing older adults’ social media adoption. Here, we identify factors that predict the use of social media among older adults (aged 68–73) and compare them to those of their adult children (aged 19–56) using population-based data from Finland. As predictors for social media use, we utilized demographic factors as well as characteristics of the respondents’ social lives. In addition, we test whether social media use in older adults is predicted by the social media use of their adult children. The data used in this study uniquely enable the study of this question because actual parent-child dyads are identifiable. In both generations, women and those with higher education were more likely to use social media. Predictors specific to men of the older generation were being divorced and younger, and predictors specific to women of the older generation were having better health and more frequent contact with friends. A higher number of children predicted use in both men and women in the older generation. As for the younger generation, specific predictors for social media use in women were younger age, divorce, higher number of children, and more frequent contact with friends. For men in the younger generation, there were no significant predictors for social media use besides higher education, which predicted social media use in all groups. Finally, social media use in a parent representing the older generation was predicted by the social media use of their adult children. This study provides novel information on the predictors of the use of social media in two family generations.

Pre-test analysis Y variable (excessive digital media consumption) results using IBM SPSS Statistics 26 for research title "Doomscrolling and Information Overload on Generation Z: Examining the Link Between Excessive Digital Media Consumption and Stress Level".

Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users, although there is rapid uptake among older age groups. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers have pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

This dataset was utilized in the analyses presented in the paper entitled "Information Bubble and Learning in the Digital Age: An Analysis from the Perspective of European and African Students." Details regarding the dataset can be found in the Methodology section of the paper.

Hong Kong Social Contact Dynamics

Understanding Age, Gender and Network Dynamics

By [source]

About this dataset

This dataset provides an in-depth look at the dynamics of social interaction, particularly in Hong Kong. It contains comprehensive information regarding individuals, households and interactions between individuals such as their ages, frequency and duration of contact, and genders. This data can be utilized to evaluate various social and economic trends, behaviors, as well as dynamics observed at different levels. For example, this data set is an ideal tool to recognize population-level trends such as age and gender diversification of contacts or investigate the structure of social networks in addition to the implications of contact patterns on health and economic outcomes. Additionally, it offers valuable insights into dissimilar groups of people including their permanent residence activities related to work or leisure by enabling one to understand their interactions along with contact dynamics within their respective populations. Ultimately this dataset is key for attaining a comprehensive understanding of social contact dynamics which are fundamental for grasping why these interactions are crucial in Hong Kong's society today

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides detailed information about the social contact dynamics in Hong Kong. With this dataset, it is possible to gain a comprehensive understanding of the patterns of various forms of social contact - from permanent residence and work contacts to leisure contacts. This guide will provide an overview and guidelines on how to use this dataset for analysis.

Exploring Trends and Dynamics:

To begin exploring the trends and dynamics of social contact in Hong Kong, start by looking at demographic factors such as age, gender, ethnicity, and educational attainment associated with different types of contacts (permanent residence/work/leisure). Consider the frequency and duration of contacts within these segments to identify any potential differences between them. Additionally, look at how these factors interact with each other – observe which segments have higher levels of interaction with each other or if there are any differences between different population groups based on their demographic characteristics. This can be done through visualizations such as line graphs or bar charts which can illustrate trends across timeframes or population demographics more clearly than raw numbers would alone.

Investigating Social Networks:

The data collected through this dataset also allows for investigation into social networks – understanding who connects with who in both real-life interactions as well as through digital channels (if applicable). Focus on analyzing individual or family networks rather than larger groups in order to get a clearer picture without having too much complexity added into the analysis time. Analyze commonalities among individuals within a network even after controlling for certain factors that could affect interaction such as age or gender – utilize clustering techniques for this step if appropriate– then focus on comparing networks between individuals/families overall using graph theory methods such as length distributions (the average number of relationships one has) , degrees (the number of links connected from one individual or family unit), centrality measures(identifying individuals who serve an important role bridging two different parts fo he network) etc., These methods will help provide insights into varying structures between large groups rather than focusing only on small-scale personal connections among friends / colleagues / relatives which may not always offer accurate portrayals due to their naturally limited scope

Modeling Health Implications:

Finally, consider modeling health implications stemming from these observed patterns– particularly implications that may not be captured by simpler measures like count per contact hour (which does not differentiate based on intensity). Take into account aspects like viral transmission risk by analyzing secondary effects generated from contact events captured in the data – things like physical proximity when multiple people meet up together over multiple days

Research Ideas

• Analyzing the age, gender and contact dynamics of different areas within Hong Kong to understand the local population trends and behavior.
• Investigating the structure of social networks to study how patterns of contact vary among socio economic backgro...