We've put together a list of the latest Truth Social statistics so you can see who uses the platform and whether or not Truth Social is likely to become a dominant social media network in the future.

A survey done in March 2022 found that 31% of Republican voters said they would use Truth Social often and 14% said they plan to use the platform a lot.

Truth Social, the social media platform launched by U.S. President Donald Trump, has seen fluctuating download numbers since its inception in February 2022. In March 2025, Truth Social recorded approximately ******* Apple App Store downloads and ******* downloads from the and Google Play Store in the United States. User adoption and brand awareness Despite its high-profile launch, Truth Social has struggled to gain widespread adoption. A May 2022 survey found that only ***** percent of U.S. respondents were registered users of the platform, with a slight gender disparity showing **** percent of male respondents having accounts compared to *** percent of female respondents. Brand awareness, however, was higher, with ** percent of U.S. social media users recognizing Truth Social when presented with its logo and name. Generational difference Truth Social's reception varies significantly across age groups and political affiliations. An October 2024 survey revealed that millennials had the most positive opinion of the platform, with **** percent viewing it favorably, followed by **** percent of Generation X and **** percent of Baby Boomers.

How does Truth Social compare to other social media platforms? There are around 2 million active Truth Social users.

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

In April 2024, Truth Social saw a total of 3.9 million desktop and mobile web visits in the United States, down from 4.8 million in March 2024. Monthly desktop and mobile web visits of the platform peaked in August 2022, reaching 9.8 million visits. Truth Social is an American media and technology company owned by former U.S. president Donald Trump.

During the beginning of the launch, they had some pretty fast growth. Here are the key Truth Social statistics you need to know.

Introduction

Truth Social Statistics: Truth Social is a social media application formed by Donald Trump, the sitting U.S. President. The platform was introduced in 2022 following Donald Trump's ban from Twitter, now X. In the first two weeks post-launch, it attracted 1 million users.

As of now, the platform has crossed 2 million active users and is expected to reach 81 million users by 2026. It was created as an alternative to major platforms such as Twitter and Facebook. The primary objective is to promote free speech, particularly for conservative perspectives. Since its inception, it has generated considerable discussion; some individuals appreciate it, while others do not.

As of September 2024, almost ** percent of Truth Social users were men, and **** percent were women. In April 2024, Truth Social saw a total of *** million desktop and mobile web visits in the United States, down from *** million in the previous month.

According to a survey conducted on April 14, 2024, 49 percent of Truth Social users had not accessed the app for over 61 days. Overall, 22 percent of users reported having accessed the social platform's app within the last seven days.

How high is the brand awareness of Truth Social in the United States?When it comes to social media users, brand awareness of Truth Social is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Truth Social in the United States?In total, * percent of U.S. social media users say they like Truth Social. However, in actuality, among the ** percent of U.S. respondents who know Truth Social, ** percent of people like the brand.What is the usage share of Truth Social in the United States?All in all, * percent of social media users in the United States use Truth Social. That means, of the ** percent who know the brand, ** percent use them.How loyal are the users of Truth Social?Around * percent of social media users in the United States say they are likely to use Truth Social again. Set in relation to the * percent usage share of the brand, this means that ** percent of their users show loyalty to the brand.What's the buzz around Truth Social in the United States?In February 2024, about * percent of U.S. social media users had heard about Truth Social in the media, on social media, or in advertising over the past four weeks. Of the ** percent who know the brand, that's ** percent, meaning at the time of the survey there's little buzz around Truth Social in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Truth Social Dataset (Limited Version)

License: CC BY 4.0

  Overview

This dataset contains posts and comments scraped from Truth Social, focusing on Donald Trump’s posts (“Truths” and “Retruths”).Due to the initial collection method, all media and URLs were excluded. Future versions will include complete post data, including images and links. Contains 31.8Million Comments, and over 18000 Posts all By Trump. As well as logged over 1.5Million unique users who commented on… See the full description on the dataset page: https://huggingface.co/datasets/notmooodoo9/TrumpsTruthSocialPosts.

As of January 20, 2023, former United States president Donald J. Trump had approximately 87.73 million followers on his Twitter accounts, registering his biggest audience across all social media platforms. Trump's profiles on Facebook and Instagram followed with 34.49 million and 23.3 million followers each. Meanwhile, his profile on his own platform Truth Social, created after he was banned from mainstream social networks at the beginning of 2021 due to inciting violence, amassed an audience of around 4.83 million followers.

Trump’s own vessel adrift

Although similar alt-tech platforms like Gab and Rumble already existed, the ban of Donald Trump from mainstream social media and the creation of his own network Truth Social were significant but brief boosts for the proliferation of alternative social platforms, which started targeting users who felt displaced or were also banned from traditional platforms. Even though receiving moderate attention during its launch, Truth Social is currently at its lowest popularity so far, with even less relevance in the public debate.

The sprawl of alt-techs

Focused on providing spaces for right-wing publics and their respective discussions, alt-tech platforms have so far only managed to gather niche audiences and limited reach. Parler’s unique monthly visitors shrank from 12.3 million in January 2021 to around 137 thousand in August 2022. Founded by Trump’s former political advisor Jason Miller, Gettr only had mild success at its start in the United States, although recently amassing more followers in countries like Brazil, especially due to its usage by supporters of former president Jair M. Bolsonaro during its last federal elections’ campaign.

Youtube social network and ground-truth communities

https://snap.stanford.edu/data/com-Youtube.html

Dataset information

Youtube (http://www.youtube.com/) is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider
such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.
(http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Network statistics
Nodes 1,134,890
Edges 2,987,624
Nodes in largest WCC 1134890 (1.000)
Edges in largest WCC 2987624 (1.000)
Nodes in largest SCC 1134890 (1.000)
Edges in largest SCC 2987624 (1.000)
Average clustering coefficient 0.0808
Number of triangles 3056386
Fraction of closed triangles 0.002081
Diameter (longest shortest path) 20
90-percentile effective diameter 6.5
Community statistics
Number of communities 8,385
Average community size 13.50
Average membership size 0.10

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-youtube.ungraph.txt.gz Undirected Youtube network
com-youtube.all.cmty.txt.gz Youtube communities
com-youtube.top5000.cmty.txt.gz Youtube communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
1,157,827.

In the SuiteSparse Matrix Collection, Problem.A is the undirected Youtube
network, a matrix of size n-by-n with n=1,134,890, which is the number of
unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 16,386
which represents the communities in the com-youtube.all.cmty.txt file.
The kth line in that file defines the kth community, and is the column
C(:,k), where C(i,k)=1 if person ...

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

According to a 2022 survey of social media users in the United States, 38 percent of Americans had heard of Parler, making it the most popular among selected alternative and alt-tech platforms. Truth Social and Telegram ranked second, with 27 percent of respondents stating they had heard of them. Rumble followed with a 20 percent awareness among respondents. Overall, 56 percent of the respondents indicated that they had heard of at least one of these platforms.

LiveJournal social network and ground-truth communities

https://snap.stanford.edu/data/com-LiveJournal.html

Dataset information

LiveJournal (http://www.livejournal.com/) is a free on-line blogging
community where users declare friendship each other. LiveJournal also
allows users form a group which other members can then join. We consider
such user-defined groups as ground-truth communities. We provide the
LiveJournal friendship social network and ground-truth communities.

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Dataset statistics
Nodes 3,997,962
Edges 34,681,189
Nodes in largest WCC 3997962 (1.000)
Edges in largest WCC 34681189 (1.000)
Nodes in largest SCC 3997962 (1.000)
Edges in largest SCC 34681189 (1.000)
Average clustering coefficient 0.2843
Number of triangles 177820130
Fraction of closed triangles 0.04559
Diameter (longest shortest path) 17
90-percentile effective diameter 6.5

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-lj.ungraph.txt.gz Undirected LiveJournal network
com-lj.all.cmty.txt.gz LiveJournal communities
com-lj.top5000.cmty.txt.gz LiveJournal communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 0-based, with nodes numbering 0 to
4,036,537.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
LiveJournal network, a matrix of size n-by-n with n=3,997,962, which is
the number of unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (0-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 664,414
which represents the communities in the com-lj.all.cmty.txt file. The kth line in that file defines the kth community, and is the column C(:,k),
where C(i,k)=1 if person nodeid(i) is in the kth community. Row C(i,:)
and row/column i of the A matrix thus refer to the same person, nodeid(i).

Ctop = Problem.aux.Communities_top5000 is n-by-5000, with the same
structure as the C array above, with the content of the
com-lj.top5000.cmty.txt file.

Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. Propaganda in social networks is disguised as legitimate news sent from authentic users. It smartly blended real users with fake accounts. We seek here to detect efforts to manipulate the spread of information in social networks, by one of the fundamental macro-scale properties of rhetoric—repetitiveness. We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. We compare them and identify statistical and network properties that distinguish between these two types of information cascades. These features are based on both the repetition distribution of hashtags and the mentions of users, as well as the network structure. Together, they enable us to distinguish (p − value = 0.0001) between the two different classes of information cascades. In addition to constructing a bipartite graph connecting words and tweets to each cascade, we develop a quantitative measure and show how it can be used to distinguish between political and non-political discussions. Our method is indifferent to the cascade’s country of origin, language, or cultural background since it is only based on the statistical properties of repetitiveness and the word appearance in tweets bipartite network structures.

Orkut social network and ground-truth communities

https://snap.stanford.edu/data/com-Orkut.html

Dataset information

Orkut (http://www.orkut.com/) is a free on-line social network where users form friendship each other. Orkut also allows users form a group which
other members can then join. We consider such user-defined groups as
ground-truth communities. We provide the Orkut friendship social network
and ground-truth communities. This data is provided by Alan Mislove et al. (http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Dataset statistics
Nodes 3,072,441
Edges 117,185,083
Nodes in largest WCC 3072441 (1.000)
Edges in largest WCC 117185083 (1.000)
Nodes in largest SCC 3072441 (1.000)
Edges in largest SCC 117185083 (1.000)
Average clustering coefficient 0.1666
Number of triangles 627584181
Fraction of closed triangles 0.01414
Diameter (longest shortest path) 9
90-percentile effective diameter 4.8

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-orkut.ungraph.txt.gz Undirected Orkut network
com-orkut.all.cmty.txt.gz Orkut communities
com-orkut.top5000.cmty.txt.gz Orkut communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
3,072,626.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
Orkut network, a matrix of size n-by-n with n=3,072,441, which is
the number of unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 15,301,901 which represents the same number communities in the com-orkut.all.cmty.txt file. The kth line in that file defines the kth community, and is the
column C(:,k), where where C(i,k)=1 if person nodeid(i) is in the kth
community. Row C(i,:) and row/column i of the A matrix thus refer to the
same person, nodeid(i).

Ctop = Problem.aux.Communities_to...

According to a survey conducted in the United States in August 2024, social media users aged over 65 years were the most likely to have used Truth Social in the run-up to the 2024 Presidential election, with eight percent of users stating to have used the social platform of Donald Trump. Overall, just two percent of social media users aged 30 to 44 years used the service for election news.