As of the fourth quarter of 2024, Meta's Threads had approximately 275 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.

Here is a breakdown of how quickly people signed up for Threads...

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.

Time spent on Threads has dropped sharply since launch

In this post, we will break down all of the latest Threads statistics and give you some insight into what the future looks like with Twitter vs Threads.

As of February 2025, almost 29 percent of Threads users were aged between 25 and 34 years. Additionally, 20.36 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 150 million downloads.

Introduction

Threads App Statistics: Meta (formerly known as Facebook) launched the Threads app in 2023 as a new social network aimed at rivalling X (previously known as Twitter). It covers microblogging, fast updates, and interaction in a simpler manner. The application gained momentum very quickly, particularly because of its connection to Instagram, which Meta also owns.

On July 6th, 2023, Threads–a messaging platform with a Twitter-like interface–was released, giving rise to rivalry between Elon Musk and Mark Zuckerberg along with their supporters. Although Twitter enacted tough new regulations, Threads was introduced with fewer limitations, attracting millions of users within one hour after its launch. As Threads matured in 2024, questions about its revenue model and growth have become significant issues within the technology industry.

Hence, it’s important to understand how the app earns money due to the never-ending competition on social media. This article aims to look into the Threads App statistics for 2025, focusing on key figures, trends, and comparisons with other social networks.

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.

As of February 2025, over 57 percent of Meta's Threads users were men. Threads was released in the summer of 2023 and is the fastest mobile app to reach 150 million downloads.

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.

This dataset provides a collection of user reviews for the Threads mobile application from both the Google Play Store and the Apple App Store. It is designed to offer insights into user satisfaction, app performance, and to help identify emerging user patterns and sentiments. The data was gathered by scraping reviews from the respective app marketplaces.

Columns

• source: Indicates the origin of the review, such as 'Google Play' or 'App Store'.
• review_description: Contains the actual text of the review provided by the user.
• rating: Represents the numerical rating given by the user.
• review_date: Specifies the date when the review was submitted.

Distribution

The dataset is typically provided in a CSV file format. Specific row or record counts are not available for the entire dataset, but review counts are detailed for various rating ranges and daily periods. For instance, 15,559 reviews are rated between 4.80 and 5.00, while 11,338 reviews were recorded between 5th and 6th July 2023.

Usage

This dataset is ideal for: * Sentiment analysis to understand overall user sentiment towards the Threads app. * Investigating factors that lead to 1-star and 5-star ratings, offering insights into user satisfaction and dissatisfaction. * Evaluating the application's performance and identifying recurring themes in user feedback.

Coverage

The dataset's geographic scope is global, collecting reviews from users worldwide. The time range for the reviews spans from 6th July 2023 to 25th July 2023. The dataset was last updated on 26th July 2023. It captures feedback from users across two major mobile platforms, Google Play (92% of reviews) and Apple App Store (8% of reviews).

License

CC-BY-NC

Who Can Use It

• Data analysts and researchers interested in mobile app performance and user sentiment.
• App developers and product managers aiming to understand user feedback for app improvements.
• Organisations conducting market research on social media applications.

Dataset Name Suggestions

• Threads Mobile App Reviews 2023
• Instagram Threads User Feedback Dataset
• Threads App Store & Google Play Reviews
• Threads User Ratings and Sentiment Data

Attributes

Original Data Source: Threads, an Instagram app Reviews

According to a 2024 survey conducted in the United States, daily users of Bluesky were more likely to use YouTube, TikTok, and X daily. Overall, Threads users are more likely to use other Meta Platforms networks every day.

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.

Dataset from our ICWSM 2017 paper. When using this resource, please use the following citation:

Aragón P., Gómez V., Kaltenbrunner A. (2017) To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion, ICWSM-17- 11th International AAAI Conference on Web and Social Media, Montreal, Canada.

@inproceedings {aragon2017ICWSM,
author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas},
title = {To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion},
booktitle = {ICWSM-17 - 11th International AAAI Conference on Web and Social Media},
publisher = {The AAAI Press},
location = {Montreal, Canada},
year = 2017
}

More info about this dataset can also be found at:

Aragón P., Gómez V., Kaltenbrunner A., (2017) Detecting Platform Effects in Online Discussions, Policy & Internet, 9, 2017.

@article{aragon2017PI,
author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas},
title = {Detecting Platform Effects in Online Discussions},
journal = {Policy \& Internet},
volume = {9},
number = {4},
pages = {420-443},
doi = {10.1002/poi3.158},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/poi3.158},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/poi3.158},
year = {2017}
}

Crawling process

We built a crawling process that collects all the stories in the front page of Meneame from 2011 to 2015 (both years included). We then performed a second crawling process to collect every comment from the discussion thread of each story. From both crawling processes, we obtained 72,005 stories and 5,385,324 comments.

It is important to highlight two issues taken into account when the crawler was designed. First, the machine-readable robots.txt file on Meneame does not disallow this process. Second, the footnote of Meneame indicates the licenses of the code, graphics and content of the website. The license for content is Attribution 3.0 Spain (CC BY 3.0 ES) which allows us to release this dataset.

Fields

Every discussion thread is stored in a JSON file named with the URL slug of the corresponding story in Meneame, located in a yyyy-mm-dd folder. The JSON file is an array of elements with the following fields:

• id (string): ID of the story/comment

• sent (timestamp): Date of the story/comment as yyyy-MM-ddThh:mm:ssZ.

• message (string): Text of the story/comment

• user (string): Username of the authoring story/comment

• karma (number): Karma score of the comment when the crawling was performed

• comments_count (number): Number of comments in reply to the story/post

• votes (number): Number of votes to the story/comment

• thread (string): URL of the thread

• thread_id (string): Sequential arriving order to the thread (0 if story, >=1 if comment)

• depth (string): Depth within the thread (0 if story, >=1 if comment)

• url (string): URL of the specific story/comment

• title (string): Title, only available for stories.

• published (string): Date when published on the front page, only available for stories.

• tags (string): Tags, only available for stories.

• clics (string): Number of clicks, only available for stories.

• users (string): Number of user votes, only available for stories.

• anonymous (string): Number of anonymous votes, only available for stories.

• negatives (string): Number of negative votes, only available for stories.

• in_reply_to_id (string): ID of the parent story/comment, only available for comments.

• in_reply_to_user (string): Authoring user of the parent story/comment, only available for comments.

• in_reply_to_thread_id (string): Sequential arriving order to the thread of of the parent story/comment, only available for comments.

Acknowledgment

This work is supported by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502).

This dataset provides details on Twitter threads, focusing on the engagement dynamics of individual tweets within a thread. It was compiled to explore the observed phenomenon where engagement metrics such as retweets, likes, and replies typically decrease with each subsequent tweet in a thread. The data offers insights into how users interact with multi-tweet content and can be used to analyse factors influencing engagement, potentially aiding in the development of strategies for optimising content on the platform. It also offers scope for Natural Language Processing (NLP) to understand how the context of a thread might affect engagement patterns.

Columns

• id: A unique identifier for each tweet.
• thread_number: An identifier used to group individual tweets belonging to the same thread.
• timestamp: The creation date and time of each tweet.
• text: The actual content of each tweet.
• retweets: The number of times each tweet was retweeted.
• likes: The number of times each tweet was liked.
• replies: The number of replies each tweet received.

Distribution

The dataset is organised into five distinct files, categorised by thread length: those with 5-10 tweets, 10-15 tweets, 15-20 tweets, 20-25 tweets, and 25-30 tweets. Each of these categories, or "bins," contains approximately 100 unique threads, resulting in around 500 threads in total. All files maintain an identical column structure. The dataset includes a substantial number of individual tweet records, with counts for different metrics like retweets and likes extending into the thousands across various value ranges. For example, there are 1,732 records with 0-63.4 likes and 1,695 records with 0-2026.7 retweets.

Usage

This dataset is ideal for: * Analysing engagement patterns within social media threads. * Conducting social science research on online communication behaviour. * Developing and testing hypotheses regarding content effectiveness on platforms like Twitter. * Exploring the influence of tweet content and context on user interaction using NLP techniques. * Informing content strategy and optimisation for social media managers and marketers.

Coverage

The dataset consists of tweets collected between October 2017 and May 2018. The data is global in scope, reflecting general Twitter activity. While no specific demographics are detailed, observations from the data collection suggest that the context or topic of threads (e.g., political vs. art threads) may influence engagement. The threads included were chosen solely based on their length, ranging from 5 to 30 tweets, irrespective of their content.

License

CC0

Who Can Use It

This dataset is suitable for: * Social media researchers and academics investigating online engagement and communication. * Data scientists and analysts performing quantitative analysis on social media data. * Marketing professionals seeking to understand and improve their social media content performance. * Natural Language Processing (NLP) practitioners interested in text analysis within a conversational context. * Students learning about data analysis and social media trends.

Dataset Name Suggestions

• Twitter Thread Engagement Analysis
• Social Media Thread Interaction Data
• Tweet Thread Dynamics Dataset
• Twitter Engagement Study

Attributes

Original Data Source: Twitter Threads

User type, thread and post count for the participating forums.

This dataset presents a collection of over 37,000 reviews for the popular New Thread mobile application, sourced from both the Google Play Store and Apple App Store. It is a meticulously curated resource designed for researchers, data scientists, and machine learning enthusiasts. The dataset facilitates in-depth analysis of user sentiments and opinions, enabling exploration of natural language processing, sentiment analysis, and app performance assessment. It provides insight into user satisfaction, usability, feature preferences, and potential areas for app improvement. Each review includes star ratings and sentiment labels, such as positive, negative, or neutral, along with essential metadata like review date and app version.

Columns

• index: A sequential identifier for each record.
• source: Indicates the platform from which the review was collected (e.g., Google Play Store, App Store).
• source (play store or app store): Specifies the broad category of the source platform.
• review_id: A unique identifier generated for each user review.
• user_name: The name of the user who submitted the review.
• review_title: A brief title provided for the review.
• review_description: The main text content of the user's review.
• rating: The star rating assigned by the user (typically from 1 to 5).
• thumbs_up: A count of validations or 'likes' provided by other users for the review.
• review_date: The date when the review was submitted.
• meta-data_1: Additional metadata related to the review, corresponding to review_date.
• developer_response: Any response provided by the app developer to the user's review.
• meta-data_2: Additional metadata related to the review, corresponding to developer_response.

Distribution

The dataset comprises over 37,000 entities or reviews, with approximately 35,000 data points originating from the Google Play Store and about 2,000 from the Apple App Store. This distribution means around 95% of the data is from Google Play and 5% from the App Store. The data file is typically provided in a CSV format. Ratings within the dataset range from 1 to 5 stars, with a significant proportion of reviews being 5-star ratings (around 17,000 reviews). The majority of reviews have a low 'thumbs_up' count.

Usage

This dataset is ideally suited for: * Conducting natural language processing (NLP) tasks. * Performing sentiment analysis to understand user opinions. * Assessing and monitoring app performance. * Benchmarking various sentiment analysis models. * Training machine learning algorithms for text classification or sentiment prediction. * Exploratory data analysis to uncover patterns and trends in user feedback. * Identifying areas for improving user experience and app features. * Gaining valuable insights into the New Thread app's reception and evolution.

Coverage

The dataset's coverage is global, encompassing user reviews from around the world. The time range for the reviews spans from early July 2023 to early August 2023, specifically from 5th July 2023 to 7th August 2023. While it includes metadata such as reviewer demographics where available, detailed demographic breakdowns are not explicitly provided within the source material.

License

CC0

Who Can Use It

• Researchers: For academic studies on mobile app user feedback, NLP, and sentiment analysis methodologies.
• Data Scientists: To build and evaluate machine learning models for sentiment detection and predictive analytics on app reviews.
• Machine Learning Enthusiasts: For hands-on practice with real-world text data, model training, and feature engineering.
• App Developers and Product Managers: To understand user perceptions, identify bugs, gather feature requests, and guide app development based on direct user feedback.
• Marketing Analysts: To gauge public sentiment towards the app and inform marketing strategies.

Dataset Name Suggestions

• Thread App Reviews Dataset
• Mobile App User Sentiment Data
• New Thread App Feedback
• App Store Review Analysis Data
• User Opinion Threads

Attributes

Original Data Source: Thread app dataset: 37000 entities

According to a survey among Vietnamese internet users conducted by Decision Lab, in the third quarter of 2024, 12 percent of respondents reported using Threads. In the same quarter, Facebook was the most used social network among surveyed respondents in Vietnam, followed by Zalo - a Vietnamese social messaging platform.

You might be surprised how much Truth Social is worth based on its small number of users.