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

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

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.

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.

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

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.

According to a survey conducted among consumers in the United States in January 2023, BeReal was the social media app seeing the highest growth in approval during the previous 90 days, approximately 23 percentage points. TextNow and Life360 followed, with an improvement of around 22 percentage points and 18 percentage points, respectively. In comparison, social video app TikTok and Truth Social saw a decrease in approval of 25.4 percentage points and 22.2 percentage points, respectively.

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.

Over the past half-century, numerous transitional justice (TJ) measures have been implemented globally. While much research has examined different TJ modalities in the aftermath of authoritarian rule and armed conflict, a growing body of work recognizes TJ outside of political transitions. We study a noteworthy export from transitional to non-transitional settings: truth commissions. Building on scholarship on TJ in established democracies, we introduce new quantitative data from the Varieties of Truth Commissions Project on truth commissions in an overlooked but significant case: the United States. The data captures 20 past, present and proposed official US truth commissions, most of them at the subnational level. Though their mandates vary considerably, they all address racial injustice, with an emphasis on anti-Indigenous and anti-Black violence. We elaborate on trends in the data and discuss the implications for unfolding efforts to reckon with historical and contemporary racial violence and injustice in the United States.

This set of files includes replication material for "How Does Transitional Justice Matter? Expanding and Refining Quantitative Research on the Effects of Transitional Justice Policies" and Online Appendix material for the same publication.

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.

Perhaps unsurprisingly, the main traffic source for false information online is social media, which generates 42 percent of fake news traffic. The nature of social networks, most notably the ease of sharing content, allows fake news to spread at a rapid rate – an issue further exacerbated by the fact that many U.S. adults sometimes believe fake news to be real.

Fake news: an ongoing problem

The presence of fake news would be less of an issue if users were more aware of how to identify it and were aware of the risks of sharing such content. Many U.S. news consumers have shared fake news online, and worryingly, ten percent did so deliberately. Adults who are part of that ten percent are just a small portion of people in the United States, and elsewhere in the world, who are responsible for spreading false information. More than 30 percent of U.S. children and teenagers have shared a fake news story online, and over 50 percent of adults in selected countries worldwide have wrongly believed a fake news story.

The result of adults and young consumers alike not only believing fake news, but actively sharing it, is that small, illegitimate websites producing such content are able to grow more successful. Such websites have the potential to tarnish or seriously damage the reputation of any persons mentioned within a fake news article, promote events or policies which do not exist, and mislead readers about important topics they are trying to keep up with. A 2019 survey revealed that most adults believe that fake news and misinformation will get worse in the next five years, and the sad truth is that this will likely be the case unless news consumers grow more discerning about what they post and share online.