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

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.

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.

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

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

Social media algorithms now shape the information we see. In 2025, over 5.4 billion people will engage with personalized feeds daily, each tailored by complex models that sort, amplify, or bury content based on real-time behavior. These algorithms affect everything from advertising campaigns to political messaging, and even how quickly...

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.

Different classes of quantifiers provably require different verification algorithms with different complexity profiles. The algorithm for proportional quantifiers, like most', is more complex than that for nonproportional quantifiers, likeall' and `three'. We tested the hypothesis that different complexity profiles affect ERP responses during sentence verification, but not during sentence comprehension. In experiment 1, participants had to determine the truth value of a sentence relative to a previously presented array of geometric objects. We observed a sentence-final negative effect of truth value, modulated by quantifier class. Proportional quantifiers elicited a sentence-internal positivity compared to nonproportional quantifiers, in line with their different verification profiles. In experiment 2, the same stimuli were shown, followed by comprehension questions instead of verification. ERP responses specific to proportional quantifiers disappeared in experiment 2, suggesting that they are only evoked in a verification task and thus reflect the verification procedure itself. The present dataset contains behavioural and EEG data from both experiments, as well as analysis scripts for both data types in R and Matlab/FieldTrip.

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems.

Data sets used for bipartite graphs network analysis.