Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.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).

Verified dataset of U.S. digital usage in 2025: internet users, social media user identities, mobile connections, and internet connection speeds.

I read a USA Today article from June 2020, where they discuss library usage during the pandemic. Some libraries set up wi-fi networks that extended outside the building, so that people would have access to the Internet even when the library was shutdown. This had me curious about how many people have convenient access to the Internet. There are some companies that rely on web pages instead of phone numbers for customer service. If someone wanted to determine the validity of claims and rumors spread by social media, they either need to have a trusted radio/television new source, or they need convenient access to the Internet to be able to investigate the information (by searching for original articles or unaltered video).

I found a pair of datasets that had information that would let me look at the situation. But while doing data cleaning, I found some problems that required significant effort to diagnose. I figured it would be useful to create a new dataset, and provide it on Kaggle in case others were interested.

I started with the dataset provided by the Institute of Museum and Library Services (IMLS), titled "IMLS Indicators Workbook: Economic Status and Broadband Availability and Adoption". The workbook contained statistics blended from three sources: the U.S. Census Bureau American Community Survey (ACS 5-year 2014-2018 estimates); broadbandnow.com (commercial aggregator of FCC data); and the Bureau of Labor Statistics (local area unemployment statistics).

On December 10, 2020, BroadbandNow.Com (bbn) provided a dataset hosted at GitHub as part of their Open Data Challenge. This had the features I wanted to cross check with the IMLS dataset.

I decided it would be worth it to do a partial clean-up of both sets, and then merge them to create a dataset with fewer problems. However, that still required some choices and compromises, so not problem-free. For example, I retained the 3 BBN features that were present in the original IMLS file, but I plan to use the information saved directly from the BBN file instead.

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

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.

Snapshot img

Social Media Listening Market Size 2025-2029

The social media listening market size is forecast to increase by USD 4.87 billion at a CAGR of 8.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing usage of social media platforms worldwide. With over 4.3 billion users as of 2021, social media has become a powerful tool for businesses to engage with their customers and gain valuable insights into consumer behavior and preferences. A key trend in this market is the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in social media listening solutions, enabling more accurate and efficient data analysis. However, this market is not without challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect user data.
Companies must ensure they have strong data security measures in place to comply with these regulations and maintain consumer trust. Additionally, the vast amount of data generated on social media requires sophisticated analytics tools to extract meaningful insights. As such, businesses seeking to capitalize on the opportunities presented by the market must invest in advanced analytics solutions and prioritize data security and privacy. By doing so, they can effectively navigate the challenges and stay ahead of the competition.

What will be the Size of the Social Media Listening Market during the forecast period?

Request Free Sample

Social media listening has emerged as a crucial business tool, enabling organizations to gain valuable insights from the vast amount of data generated through social media activity. This data is analyzed using techniques such as topic modeling and sentiment scoring to understand consumer behavior, preferences, and trends. Social media geographics and demographics provide essential context, while social media reach and volume measure the scope and impact of conversations. Social media pulse and sentiment reflect the current sentiment and buzz surrounding specific topics, offering real-time insights into market dynamics and trends.
Social media listening software is a vital component of the global market for social media analytics. Social media influence is assessed through the size and engagement of an audience, providing valuable information for marketing and brand management strategies. The social media landscape and heatmap offer a comprehensive view of the social media ecosystem, helping businesses stay informed and adapt to evolving patterns.

How is this Social Media Listening Industry segmented?

The social media listening industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Software
  Services


End-user

  Retail and e-commerce
  IT and telecom
  BFSI
  Media and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The software segment is estimated to witness significant growth during the forecast period. This segment encompasses platforms and tools that offer real-time, automated, and scalable capabilities to monitor and analyze social media conversations across various channels such as Twitter, Facebook, Instagram, LinkedIn, TikTok, and Reddit. Real-time monitoring is a key feature of these solutions, empowering brands to identify mentions, trends, and sentiment as they emerge. By staying abreast of evolving topics, businesses can respond promptly to customer concerns, capitalize on viral events, and maintain a strong online presence. Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to social media listening software, enabling advanced topic identification, sentiment analysis, and trend recognition.

These technologies enable businesses to gain valuable customer insights, inform product development, and enhance customer experience. Social media listening platforms also offer data visualization and reporting features, allowing businesses to analyze and present their findings in a clear and actionable manner. Additionally, they provide social media dashboards, alerts, and governance tools to ensure compliance with social media policies and ethical standards. In summary, social media listening software plays a pivotal role in the global market for social media analytics, offering real-time insights and advanced capabilities to help businesses navigate the complex social media landscape and engage effectively with their audience.

Get a glance at the market report of share of v

This survey charted the gambling, social media usage and subjective well-being of young people aged 15-25 years in the United States. The study was conducted as part of the "Problem Gambling and Social Media: Social Psychological Study on Youth Behaviour in Online Gaming Communities" research project. The aim of the project was to analyse how young social media users evaluate, adopt and share gambling-related online content and how online group processes affect their gambling and gambling-related attitudes. FSD's holdings also include two other datasets that were collected using a nearly identical questionnaire (FSD3399 and FSD3400). Data for the research project have been collected in Finland, the United States, Spain, and South Korea. First, the respondents were asked which social media services they used (e.g. Facebook, YouTube, Instagram, discussion forums, online casinos) and how often. Topics that the respondents discussed on gambling-related social media were charted more closely, and they were asked, for example, whether the discussion usually related to instructions or tips on gambling or to problem gambling and recovering from problem gambling. Some questions on the respondents' social media activity were also presented, for instance, how often they saw gambling-related advertising online, how often they changed their most important social media passwords, and how often they uploaded pictures of themselves on social media. The respondents were asked whether they had ever been harassed online or had been the victim of a crime on the Internet in the past three years (e.g. defamation, identity theft, fraud, sexual harassment). The respondents' identity bubbles on social media were surveyed by using the IBR scale (Identity Bubble Reinforcement Scale). The respondents were asked, for instance, whether they thought they could be themselves on social media and whether they only interacted with people similar to them on social media. Additionally, the CIUS scale (Compulsive Internet Use) was used to examine problems related to Internet use. Questions focused on, for example, whether the respondents found it difficult to stop using the Internet when they were online, whether people close to them said they should use the Internet less, and whether they felt restless, frustrated or irritated when they couldn't use the Internet. In the next section of the questionnaire, the respondents were randomly assigned to two groups for a vignette experiment. Respondents in the test group were told they belong to Group C because they had answered the earlier questions in a similar manner to others in the group. Those in the control group were given no information on the group. The respondents were presented with different gambling-related social media scenarios, and they were asked to evaluate the contents of the gambling-related messages by "liking" or "disliking" the message or by not reacting to it at all. Each respondent was shown four different gambling messages with different contents. Three factors were manipulated in the scenarios (2x2x2 design): expressed stance of the message on gambling (positive or negative), narrative perspective of the message (experience-driven first-person narration or fact-driven third-person narration) and majority opinion of other respondents on the message (positively or negatively biased distribution of likes or dislikes). For Group C, the majority opinion was seemingly provided by other Group C members, whereas for the control group the majority opinion was seemingly provided by other respondents. Additionally, the respondents' attitudes towards the message were surveyed with statements regarding, for instance, how likely they would find the message interesting or share it on social media. Next, the respondents' attitudes towards gambling were charted by using the ATGS scale (Attitudes Towards Gambling Scale). They were asked, for example, whether people should have the right to gamble whenever they want, whether most people who gamble do so sensibly and whether it would be better if gambling was banned altogether. The respondents' gambling habits were examined by using the SOGS scale (South Oaks Gambling Screen), and they were asked, for instance, which types of gambling they had done in the past 12 months (played slot machines, visited an online casino, bet on lotteries etc.), whether the people close to them had gambling problems, and whether they had borrowed money to gamble or to pay gambling debts. In addition, the respondents' alcohol consumption was surveyed with a few questions from the AUDITC scale (The Alcohol Use Disorders Identification Test), and they were asked whether they had used various drugs for recreational purposes (e.g. cannabis, LSD, amphetamine, opioids) and which online resources they had used to purchases these drugs (e.g. Facebook, Instagram, Craigslist). The respondents' subjective well-being and social relationships were examined next. The respondents were asked how happy they were in general and how satisfied they were with their economic situation and life in general. They were also asked how well the single statement "I have high self-esteem" from the SISE scale (Single-item Self-esteem Scale) described them. The three statements on lacking companionship, feeling left out and feeling isolated from the LONE scale (Three-item Loneliness Scale) were also included in the survey. Feelings of belonging to different groups or communities (e.g. family, friends, neighbourhood, parish/religious community) were charted, and the 12-item GHQ scale (General Health Questionnaire) was used to survey the respondents' recent mental health. Questions included, for example, whether the respondents had been able to concentrate on what they were doing, had felt they couldn't overcome their difficulties, and had been losing confidence in themselves. Finally, the respondents' sense of control over the events in their lives was examined with the MASTERY scale (Sense of Mastery Scale), with questions focusing on, for instance, whether they thought they had little control over the things that happen to them and whether they often felt helpless in dealing with the problems of life. The respondents' impulsivity was surveyed by using the EIS scale (Eysenck Impulsivity Scale) and their willingness to delay gratification was surveyed with the GRATIF scale (Delay of Gratification). Background variables included the respondent's gender, age, country of birth (own and parents') level of education, type of municipality of residence, household composition, disposable income, possible financial problems, and economic activity and occupational status.

Techsalerator’s Location Sentiment Data for the United States of America

Techsalerator’s Location Sentiment Data for the United States offers a comprehensive dataset crucial for businesses, researchers, and technology developers. This dataset provides deep insights into location-based sentiment patterns, helping users understand regional and local variations in public opinion across different areas in the U.S.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for the United States

Techsalerator’s Location Sentiment Data for the United States provides structured sentiment analysis across urban, suburban, and rural areas. This dataset is essential for AI development, market research, political analysis, and social studies.

Top 5 Key Data Fields

• Location of Sentiment – Identifies the geographic location where the sentiment was recorded, helping researchers analyze sentiment variations across regions.
• Sentiment Score – Measures the positive or negative sentiment expressed in a specific location, useful for gauging public opinion on various topics.
• Time of Sentiment – Records the exact time and date when the sentiment was captured, helping to track trends over time, such as during elections or major events.
• Sentiment Source – Categorizes the source of sentiment data, including social media posts, customer reviews, surveys, and news articles.
• Sentiment Context – Provides insights into the context surrounding the sentiment, helping to understand the cause behind positive or negative responses (e.g., political events, economic factors).

Top 5 Location Sentiment Trends in the United States

• Political Sentiment Shifts – Election years show significant changes in sentiment, with variations across states, influencing campaign strategies and policy decisions.
• Economic Influence on Sentiment – Economic downturns or booms can significantly affect sentiment, particularly in regions reliant on specific industries like agriculture or manufacturing.
• Urban vs. Rural Sentiment Differences – Sentiment trends often differ between urban and rural areas, with urban centers focusing on issues like housing, healthcare, and public services, while rural areas tend to emphasize agricultural policies and infrastructure.
• Impact of Social Movements – Events like protests, social justice movements, and activism impact sentiment, with regional differences reflecting local engagement and issues.
• Disaster-Related Sentiment – Natural disasters and their aftermath, such as hurricanes, wildfires, and floods, lead to changes in public sentiment, influencing recovery and support strategies.

Top 5 Applications of Location Sentiment Data in the United States

• Market Research – Businesses use sentiment data to assess regional customer perceptions, helping to tailor marketing strategies for different areas.
• Political Campaigns – Political analysts and candidates use sentiment data to gauge public opinion, identify key issues, and shape campaign messages.
• Crisis Management – Sentiment analysis helps organizations understand public sentiment during crises (e.g., natural disasters, pandemics) and tailor responses accordingly.
• Urban Planning – Local governments use sentiment data to identify public concerns and priorities, guiding urban development and policy-making.
• Consumer Behavior Analytics – Retailers and service providers leverage sentiment data to track customer feedback and adjust products or services based on regional preferences.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for the United States, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Location of Sentiment
• Sentiment Score
• Time of Sentiment
• Sentiment Source
• Sentiment Context
• Sentiment Type (Positive, Negative, Neutral)
• Demographic Information (Age, Gender, etc.)
• Topic Categorization (Political, Economic, Social, etc.)
• Sentiment Trends Over Time
• Contact Information

For detailed insights into location-based sentiment patterns across the United States, Techsalerator’s dataset is an invaluable resource for researchers, marketers, political analysts, and urban planners.

Version 25 of the dataset, we have refactored the full_dataset.tsv and full_dataset_clean.tsv files (since version 20) to include two additional columns: language and place country code (when available). This change now includes language and country code for ALL the tweets in the dataset, not only clean tweets. With this change we have removed the clean_place_country.tar.gz and clean_languages.tar.gz files. With our refactoring of the dataset generating code we also found a small bug that made some of the retweets not be counted properly, hence the extra increase on tweets available.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (651,611,876 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (154,646,580 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

Content

Social media statistics for the various members and parties of the American Congress including stats about number of active users, party member, max number of followers, social media platform used, etc.

This table includes platform data for Facebook participants in the Deactivation experiment. Each row of the dataset corresponds to data from a participant’s Facebook user account. Each column contains a value, or set of values, that aggregates log data for this specific participant over a certain period of time.

Context

The Open Data 500, funded by the John S. and James L. Knight Foundation (http://www.knightfoundation.org/) and conducted by the GovLab, is the first comprehensive study of U.S. companies that use open government data to generate new business and develop new products and services.

Study Goals

• Provide a basis for assessing the economic value of government open data

• Encourage the development of new open data companies

• Foster a dialogue between government and business on how government data can be made more useful

The Govlab's Approach

The Open Data 500 study is conducted by the GovLab at New York University with funding from the John S. and James L. Knight Foundation. The GovLab works to improve people’s lives by changing how we govern, using technology-enabled solutions and a collaborative, networked approach. As part of its mission, the GovLab studies how institutions can publish the data they collect as open data so that businesses, organizations, and citizens can analyze and use this information.

Company Identification

The Open Data 500 team has compiled our list of companies through (1) outreach campaigns, (2) advice from experts and professional organizations, and (3) additional research.

Outreach Campaign

• Mass email to over 3,000 contacts in the GovLab network

• Mass email to over 2,000 contacts OpenDataNow.com

• Blog posts on TheGovLab.org and OpenDataNow.com

• Social media recommendations

• Media coverage of the Open Data 500

• Attending presentations and conferences

Expert Advice

• Recommendations from government and non-governmental organizations

• Guidance and feedback from Open Data 500 advisors

Research

• Companies identified for the book, Open Data Now

• Companies using datasets from Data.gov

• Directory of open data companies developed by Deloitte

• Online Open Data Userbase created by Socrata

• General research from publicly available sources

What The Study Is Not

The Open Data 500 is not a rating or ranking of companies. It covers companies of different sizes and categories, using various kinds of data.

The Open Data 500 is not a competition, but an attempt to give a broad, inclusive view of the field.

The Open Data 500 study also does not provide a random sample for definitive statistical analysis. Since this is the first thorough scan of companies in the field, it is not yet possible to determine the exact landscape of open data companies.

Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

Data Sources:

1. GitHub: Access comprehensive data about GitHub repositories, developer profiles, contributions, issues, social interactions, and more.

2. StackShare: Receive information about companies, their technology stacks, reviews, tools, services, trends, and more.

3. DockerHub: Dive into data from container images, repositories, developer profiles, contributions, usage statistics, and more.

Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

With our datasets, you'll receive:

• Usernames;
• Companies;
• Locations;
• Job Titles;
• Follower Counts;
• Contact Details;
• Employability Statuses;
• And More.

Choose from various output formats, storage options, and delivery frequencies:

• Get datasets in CSV, JSON, or other preferred formats.
• Opt for data delivery via SFTP or directly to your cloud storage, such as AWS S3.
• Receive datasets either once or as per your agreed-upon schedule.

Why choose our Datasets?

1. Fresh and accurate data: Access complete, clean, and structured data from scraping professionals, ensuring the highest quality.

2. Time and resource savings: Let us handle data extraction and processing cost-effectively, freeing your resources for strategic tasks.

3. Customized solutions: Share your unique data needs, and we'll tailor our data harvesting approach to fit your requirements perfectly.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is trusted by Fortune 500 companies and adheres to GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Empower your data-driven decisions with Oxylabs Developer Community and Code Datasets!

Facebook Users Engagement Analysis Author: Tamara Banaim Dataset: Pseudo Facebook Dataset (Kaggle, uploaded to Hugging Face)

  Overview-

This project analyzes data from 99,003 Facebook users, focusing on demographic information and engagement metrics such as likes given, likes received, friend count, and account tenure. The analysis explores how age and user activity are related, and what factors influence engagement on the platform.

  Objective-

To examine how age and… See the full description on the dataset page: https://huggingface.co/datasets/tamarabanaim/facebook-users-data.

Jojo Siwa's Tweets

Analyzing Jojo Siwa’s Reach and Impact on Social Media

By Twitter [source]

About this dataset

This dataset provides a comprehensive understanding of Jojo Siwa's Twitter content and engagement. Through detailed analysis, we can see the types of messages she posts, which ones receive the most likes or comments, and other insights into her social media strategy. The data spans from November 2018 to March 2021 and includes over 2,500 tweets posted by Siwa during this period, including text-based messages, images, quotes, outlinks, as well as other media. Using this data allows us to gain insight into how her posts are received by her followers on Twitter compared to other platforms. Our dataset’s columns include information on number of likes/retweets each tweet has received as well as whether the message contained any media like images or videos along with its captions. Utilize this powerful data set today to get a better handle on Jojo Siwa's popular presence on social media!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the Jojo Siwa’s Twitter Insights dataset, where you can gain an insight into how she is engaging with her fanbase on social media. This dataset provides valuable information about her posts—including likes, media, and engagement statistics—and covers over two years of data for easy analysis. Here’s a quick guide to using this dataset:

Research Ideas

• Examining engagement rates of different visual media types used in Jojo Siwa’s tweets (photos, graphics, GIFs, etc.), to determine what type of content has the highest engagement.
• Analyzing which topics and themes are the most popular among Jojo Siwa’s followers by examining the hashtags and keywords used in her tweets.
• Investigating trends in engagement throughout different periods of time by analyzing how much each tweet was liked compared to how many followers she has at that period of time

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

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

According to Cognitive Market Research, the global Social Commerce Market size was USD 769485.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 32.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 307794.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 30.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 230845.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 176981.60 million in 2024 and will grow at a compound annual growth rate (CAGR) of 34.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 38474.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 31.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 15389.70 million in 2024 and will grow at a compound annual growth rate (CAGR) of 31.9% from 2024 to 2031.
The business to consumer (B2C) held the highest Social Commerce Market revenue share in 2024.

Market Dynamics of Social Commerce Market

Key Drivers for Social Commerce Market

The rise of social media is driving the growth of social commerce: The growth of social commerce is heavily influenced by the rise of social media and its increased usage on mobile devices. Social media platforms such as Facebook, Instagram, TikTok and WhatsApp have become major hubs for online shoppers. Customers are more likely to find and buy products directly from social media platforms than from traditional e-commerce websites since they spend more time on these channels.

For instance,

 5.24 billion use social media worldwide, as of January, 2025.Facebook remains to be the leading social media platform with over 3 billion monthly active users, followed by YouTube with 2.5 billion and Instagram with 2 billion monthly active users.
90% of consumers rely on social media to keep up with trends and cultural moments and nearly half of them interact with brands more often on social media platforms. 

(Source: https://backlinko.com/social-media-users)

https://sproutsocial.com/insights/social-media-statistics/#social-media-usage-statistics)

Advancements in Technology to Propel Market Growth: The Social Commerce Market has witnessed steady growth, driven by advancements in technology. Mobile technology has advanced significantly over the last ten years, and smartphones are now an essential part of people's everyday lives. Users are increasingly choosing to shop straight from their smartphones because to improved smartphone capabilities and internet connectivity, which has expanded mobile commerce. Additionally, more people are using smartphones due to rising disposable incomes, which ultimately enhances the social commerce market value environment.

Key Restraint for the Social Commerce Market

Growing concerns around data privacy restrict market growth: Concerns regarding privacy are hindering the expansion of social commerce, as individuals are reluctant to disclose financial or personal information on various platforms. The apprehension surrounding data breaches and potential misuse diminishes trust and affects transaction volumes. It is imperative for companies to implement secure and transparent practices in order to maintain user confidence and adhere to the growing data protection regulations globally.

Key Trends for the Social Commerce Market

The Emergence of Live Shopping and Influencer Commerce: Live shopping and promotions led by influencers are revolutionizing social media platforms into dynamic marketplaces. Consumers are increasingly placing their trust in the product reviews and recommendations provided by creators, which enhances both engagement and conversion rates. This phenomenon is particularly evident on TikTok and Instagram, where influencers conduct interactive shopping events that are directly connected to in-app purchasing.

AI-Enhanced Personalization and Intelligent Chatbots: Social commerce platforms are utilizing artificial intelligence to offer tailored product suggestions and efficient customer support. Intelligent chatbots deliver immediate assistance, thereby enhan...

The metrics in this dataset measure users who viewed posts with links to civic news URLs. The dataset contains URL-level metrics from Facebook activity data for adult U.S. monthly active users, aggregated over the study period. Includes content views, audience size, content attributes, user attributes.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, 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.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).Find more key insights for the number of Twitter users in countries like Canada and Mexico.