Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

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The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

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Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

In 2025, marketing and SEO will continue to evolve with emerging technologies, shifting consumer behaviors, and new search engine algorithms. Businesses must adapt by focusing on personalized experiences, voice search optimization, AI-driven strategies, and mobile-first approaches. For example, AgencySpotter emphasizes the importance of targeted web traffic to ensure your marketing efforts reach the right audience. To stay competitive, embracing content quality, user engagement, and data-driven insights will be key to driving traffic, increasing conversions, and achieving long-term success. Understanding these trends and implementing them will help businesses navigate the dynamic digital landscape of 2025 and beyond.

For more tips and updates, check out our Pinterest here.

Subway Restaurant Locations in the United States

Analyzing the Location of Subway Stores in the United States

About this dataset

Did you know that more than half of all Subway locations are in the United States? With such a large presence here, it's no wonder that Americans love their sandwiches!

This dataset is perfect for anyone wanting to know where their nearest Subway is, or for researchers wanting to understand more about the distribution of Subway stores across the United States. The data includes the store name, street address, city, state, and ZIP code, and the store's latitude and longitude coordinates. There is also contact information such as phone numbers and email addresses, and social media links for Subway's Facebook, Twitter, Instagram, and Pinterest pages

How to use the dataset

This dataset is perfect for anyone wanting to know where their nearest Subway is, or for researchers wanting to know more about the distribution of Subway stores across the United States.

The data includes the store name, street address, city, state, and ZIP code, and the store's latitude and longitude coordinates. There is also contact information such as phone numbers and email addresses, and social media links for Subway's Facebook, Twitter, Instagram, and Pinterest pages.

How to use the dataset:

This dataset can be used to find out the nearest Subway store from a given location. Market researchers can also use it to understand the geographic distribution of fast-food franchises and how this varies by country.

Research Ideas

• Use the dataset to plan the expansion of the Subway franchise into new markets.
• Use the dataset to understand the geographic distribution of fast food franchises and how this varies by country.
• Use the dataset to investigate whether there are any patterns in the location of Subway stores that might be indicative of socio-economic factors such as income or educational level

Data Source

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - 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. - You may not: - Use the material for commercial purposes.

Columns

File: subway.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------| | name | The name of the Subway store. (String) | | url | The URL of the Subway store's website. (String) | | street_address | The street address of the Subway store. (String) | | city | The city in which the Subway store is located. (String) | | state | The state in which the Subway store is located. (String) | | zip_code | The ZIP code of the Subway store. (String) | | country | The country in which the Subway store is located. (String) | | phone_number_1 | The first phone number of the Subway store. (String) | | phone_number_2 | The second phone number of the Subway store. (String) | | fax_1 | The first fax number of the Subway store. (String) | | fax_2 | The second fax number of the Subway store. (String) | | email_1 | The first email address of the Subway store. (String) | | email_2 | The second email address of the Subway store. (String) | | open_hours | The hours of operation for the Subway store. (String) | | latitude | The latitude coordinate of the Subway store. (Float) | | longitude | The longitude coordinate of the Subway store. (Float) | | facebook | The URL of the Subway store's Facebook page. (String) | | twitter | The URL of the Subway store's Twitter page. (String) | | instagram | The URL of the Subway store's Instagram page. (String) |

This statistic shows a ranking of the estimated number of Pinterest users in 2020 in Africa, differentiated by country. The user numbers 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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Image: https://br.pinterest.com/pin/595812225683643743/

"Their dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.

• Question: A train running at the speed of 48 km / hr crosses a pole in 9 seconds . what is the length of the train ? • Rationale: Speed = ( 48 x 5 / 18 ) m / sec = ( 40 / 3 ) m / sec . length of the train = ( speed x time ) . length of the train = ( 40 / 3 x 9 ) m = 120 m . answer is c . • Options: a ) 140 , b ) 130 , c ) 120 , d ) 170 , e ) 160 • Correct Option is: C

The rationales are noisy, incomplete and sometimes incorrect. We correct these rationales and provide stepwise solutions for a portion of AQuA-RAT.

• Their Annotated Formula: multiply(divide(multiply(48, const_1000), const_3600), 9)

Hey Guys.

Here I collect more than 2000 portrait faces of humans which are downloaded from the Google search engine and Pinterest and so on.

here you are able to upload your face and check it by deep learning model which is can detect whether your face is happy or sad.

file formats are : jpg - jpeg - png - svg

The number of Pinterest users in the Philippines was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+28.3 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 1.39 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, 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).Find more key insights for the number of Pinterest users in countries like Indonesia and Laos.

The number of LinkedIn users in Africa was forecast to continuously increase between 2024 and 2028 by in total 37 million users (+68.13 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 91.29 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, 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).Find more key insights for the number of LinkedIn users in countries like South America and Caribbean.

The number of Reddit users in Africa was forecast to continuously increase between 2024 and 2028 by in total 4.7 million users (+66.67 percent). After the eighth consecutive increasing year, the Reddit user base is estimated to reach 11.78 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, 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. Reddit users encompass both users that are logged in and those that are not.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 Reddit users in countries like North America and Asia.

This statistic shows a ranking of the estimated number of Twitter users in 2020 in Africa, differentiated by country. The user numbers 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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).