As of the third quarter of 2025, Meta's Threads had approximately 400 million monthly active users (MAU), up from 200 million MAU in the third quarter of 2024. Just 24 hours after the release of Threads in July 2023, 30 million people were using the new network from Meta Platforms.

As of February 2025, almost ** percent of Threads users were aged between 25 and 34 years. Additionally, ***** percent of users were between the aged of 18 and 24 years. Threads was released in the summer of 2023 and is the fastest mobile app to reach *** million downloads.

As of February 2025, over ** percent of Meta's Threads users were men. Threads was released in the summer of 2023 and is the fastest mobile app to reach *** million downloads.

Meta Platforms released a new social media platform called Threads on 5th July 2023, which is available in over 100 countries, excluding the European Union. Within two hours of its release, two million users had signed up for Threads, and within 24 hours, 30 million people were on the new online network. As of July 10th, the number of sign-ups reached 100 million. Users can post up to 500 characters on Threads, and the service offers numerous features that are comparable to those on Twitter and is verified via users' Instagram accounts.

Threads has emerged as a rapidly expanding social platform since its launch in July 2023, drawing millions in just days. Its features now extend beyond microblogging, with evolving communication tools reshaping how people connect online. In customer research and brand engagement, marketers in media and entertainment leverage Threads to spark real-time...

The Threads, an Instagram App Reviews dataset is a comprehensive collection of user reviews from the Threads mobile app on Google Play Store & App Store, capturing valuable insights and sentiments. The dataset enables the understanding of user satisfaction, evaluation of app performance, and identification of emerging patterns.

The way data was collected

Scraping Threads App reviews on Google Play Store & App Store

Ideas for using this dataset

• Sentiment analysis
• What makes the application receive 1-star and 5-star

Note - It was last updated on July 26th 2023

On 5th July 2023, Meta Platforms released Threads, a text-focused social media platform. Joining Meta's Family of Apps, Threads focuses on real-time conversations, giving users a similar experience to Twitter. Threads offers users 500 characters per post, in comparison to Twitter's 280 characters. However, for Twitter Blue subscribers, posts can be substantially longer. Threads users can also take advantage of longer video posts, and free verification through their Instagram accounts.

A bolt out of the (Twitter) blue

As the tech elite attempt to fill the Twitter-shaped hole that social media users are feeling since the platform’s recent changes, the company is offering users the chance to verify themselves on the platform. The famous blue check mark, once reserved for journalists, celebrities, and public figures, can be purchased for a monthly fee. As of April 2023, there 640 thousand people were subscribing to Twitter Blue, up from 290 thousand in February 2023.

Threads is off to a strong start

Creating a profile on Threads is easy for Meta product users who already have an Instagram account as the new and free-to-use platform is integrated into the widely used app. With around two billion monthly active users on Instagram, it is no surprise that Threads gained 30 million sign-ups within 24 hours of being launched. Although signing up for Threads is easy, quitting the platform is not so straightforward, as deleting a Threads account results in the deactivation of the linked Instagram account.

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F08688f046820175d6cb116271bd6f2a2%2Fthreads2.png?generation=1700495055256354&alt=media" alt="">

As per wikipedia page

Threads is an online social media and social networking service operated by Meta Platforms. The app offers users the ability to post and share text, images, and videos, as well as interact with other users' posts through replies, reposts, and likes. Closely linked to Meta platform Instagram and additionally requiring users to both have an Instagram account and use Threads under the same Instagram handle, the functionality of Threads is similar to X (formerly known as Twitter). The application is available on iOS and Android devices; the web version offers limited functionality and requires a mobile app install first. It is the fastest-growing consumer software application in history, gaining over 100 million users in its first five days, surpassing the record previously set by ChatGPT. Its early success was not sustained and the user base of the app plummeted more than 80% to 8 million daily active users by the end of July.

These reviews were extracted from its Google Store page.

Usage

This dataset should paint a good picture on what is the public's perception of the app over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of the app had the most positive feedback, the worst.
3. Use topic modeling to identify the pain points of the application.

(AND MANY MORE!)

Images

Images generated using Bing Image Generator

threads.net is ranked #348 in TW with 5.67M Traffic. Categories: . Learn more about website traffic, market share, and more!

As of July 21, 2023, Meta's Threads had around 13 million daily active users, down from 44 million DAU on July 7th, 2023. The new social network was launched on July 5, 2023, and within five days of its release, had amassed around 100 million signups.

threads.com is ranked #14 in TW with 206.78M Traffic. Categories: Apparel and Fashion. Learn more about website traffic, market share, and more!

According to a 2024 survey conducted in the United States, Bluesky users were more likely than Threads users to say that AI would have at least a somewhat positive impact on their overall quality of life over the next *** years.

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 ...

Context

When Twitter introduced its thread functionality, a debate emerged: "If you're gonna write a f*ck ton of tweets at once, why not write a blog post instead of cluttering my feed?"... "It's easier and user-friendlier to share ideas in a single app"...

I'm not getting into that debate. Both blog posts and Twitter threads have their own advantages.

But I noticed a phenomenon while reading threads on Twitter: the engagement—*retweets, likes and replies*—drops with each subsequent tweet!

Now, this has some logical explanations. Like, people don't want to retweet or like every tweet in a thread, because that'd be annoying. But this trend kept appearing in every single thread I read.

It was bugging me, so I had to gather some data.

Content

The dataset is divided into five parts:
- five_ten.csv: data of threads 5-10 tweets long
- ten_fifteen.csv: data of threads 10-15 tweets long
- fifteen_twenty.csv: data of threads 15-20 tweets long
- twenty_twentyfive.csv: data of threads 20-25 tweets long
- twentyfive_thirty.csv: data of threads 25-30 tweets long

They all contain the same data:
- id: Tweet ID (maybe I should remove it to anonymize the data?)
- thread_number: Thread identifier, used for grouping each thread and its tweets
- timestamp: Creation date of each tweet - text: The content of each tweet
- retweets: Retweet count for each tweet
- likes: Like count for each tweet
- replies: Reply count for each tweet

Each "bin" contains around 100 threads... so in total there are ~500 threads.

Acknowledgements

The threads were manually gathered using Thread Reader (both the web page and the bot).

Disclaimer

The content of the threads/tweets did not had any influence in choosing a thread or not. The only parameter was the length of the thread (5-30 tweets tops). The tweets collected date from October 2017 to May 2018.

Inspiration

Some things I noticed while gathering the data was that political threads have a steadier engagement than, say, art threads. So context might influence thread engagement, and it'd be interesting to do some NLP to figure that out.

Also it'd be cool to find a "formula" for better engagement in Twitter threads, like how long should a thread be? or maybe a probability of engagement based on the success of the initial tweet?

Finally, this whole issue reminds me of the headline problem: most people don't go beyond the headline. Maybe Twitter threads suffer from that too.

As of July 21, 2023, Twitter - now rebranded into X - counted approximately 106 million users who interacted with the app daily on Android. In comparison, Meta's latest product Threads counted around 13 million users engaging with the app on a daily basis.

Dataset Card for Reddit threads

  Dataset Summary

The Reddit threads dataset contains 'discussion and non-discussion based threads from Reddit which we collected in May 2018. Nodes are Reddit users who participate in a discussion and links are replies between them' (doc).

  Supported Tasks and Leaderboards

The related task is the binary classification to predict whether a thread is discussion based or not.

  External Use





  PyGeometric

To load in… See the full description on the dataset page: https://huggingface.co/datasets/graphs-datasets/reddit_threads.

Digital Thread Market size was valued at USD 448.6 Million in 2024 and is projected to reach USD 1,589.2 Million by 2032 growing at a CAGR of 17.1% from 2025 to 2032.

Global Digital Thread Market Drivers

Digital Transformation in Manufacturing Industries: Manufacturing businesses are increasingly using digital threads to increase efficiency and production. According to the National Institute of Standards and Technology (NIST), digital threads enable continuous data flow in manufacturing, sustaining a $2 trillion global sector, of which the United States accounts for a sizable portion.

Increasing Focus on Supply Chain Optimization: The demand for improved supply chain management is a primary driver of the Digital Thread market. According to a McKinsey analysis from 2020, digital transformation in the supply chain could save operational costs by 10-20%, spurring growing adoption of digital thread technology to optimize logistics and inventory management.

Rising Demand for Real-Time Data Analytics: The growing demand for real-time, data-driven decision-making is driving the use of digital thread technology. According to the World Economic Forum, global usage of AI and real-time analytics is predicted to increase by 40% by 2025, driving up demand for digital threads in industries.

Relative importance of the three factors as predictors of frequency change (), calculated using the method of Ref. [62]. Importance is based on the fraction of the variance of explained by each factor. This method conservatively estimates the relative importance of the independent variables in a multiple regression setting. The data are combined over all window pairs considered in Figure 3. To avoid artifactual correlations for small and large , the range of words is restricted in , as indicated in the caption of Figure 5.

Automotive Thread Sealants Market was valued at USD 1.27 Billion in 2024 and is projected to reach USD 1.94 Billion by 2032, growing at a CAGR of 5.5% during the forecast period from 2026-2032.Global Automotive Thread Sealants Market DriversThe market drivers for the Automotive Thread Sealants Market can be influenced by various factors. These may include:Growing Vehicle Production Volumes: Rising global automotive manufacturing, particularly in emerging markets, drives demand for thread sealants used in engine assembly, transmission systems, and other critical components requiring leak-proof connections and long-term durability.Stricter Emission Standards: Increasingly stringent environmental regulations require better sealing solutions to prevent fluid leaks that could impact emissions. Thread sealants help manufacturers meet these standards by ensuring reliable seals in fuel systems and exhaust components.Electric Vehicle Adoption: The shift toward electric vehicles creates new sealing requirements for battery cooling systems, charging infrastructure, and high-voltage components. Specialized thread sealants designed for these applications represent a growing market segment with unique performance demands.