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

In November 2024, Truth Social generated around 427,000 downloads from the Apple App Store and the Google Play Store in the United States combined. The social media app Truth Social, officially launched on February 21, 2022, saw approximately one million downloads in the remaining days of February 2022. The app, which became available to Android users in October 2022, saw around 353 thousand downloads from Android users in the first month of its release from the Google Play Store. The Truth Social app, which is published by T Media Tech LLC, and it is owned by Trump Media & Technology Group, went public on the New York Stock Exchange on March 26, 2024, at a staggering valuation of almost eight billion U.S. dollars.

In May 2022, an online survey in the United States found that one percent of respondents who identified as Democrats had an account on Truth Social, the social media app launched by former U.S. President Donal Trump. Additionally, approximately four percent of Republican respondents were registered users of Truth Social.

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.

As of September 2024, over 21 percent of global Truth Social users belonged to the 55 to 64 year age group, making this the largest audience base of the social media platform owned by former president Donald Trump. Users aged 18 to 24 years accounted for just nine percent of users.

A Truth Social data set containing a network of users, their associated posts, and additional information about each post. Collected from February 2022 through September 2022, this dataset contains 454,458 user entries and 845,060 Truth (Truth Social’s term for post) entries.

Comprised of 12 different files, the entry count for each file is shown below.

File	Data Points
users.tsv	454,458
follows.tsv	4,002,115
truths.tsv	823,927
quotes.tsv	10,508
replies.tsv	506,276
media.tsv	184,884
hashtags.tsv	21,599
external_urls.tsv	173,947
truth_hashtag_edges.tsv	213,295
truth_media_edges.tsv	257,500
truth_external_url_edges.tsv	252,877
truth_user_tag_edges.tsv	145,234

A readme file is provided that describes the structure of the files, necessary terms, and necessary information about the data collection.

As of September 2024, almost 62 percent of Truth Social users were men, and 38.3 percent were women. 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 the previous month.

In May 2022, an online survey in the United States found that only three percent of respondents were registered users of social media app Truth Social. Truth Social was launched in February 2022 by former U.S. President Donald Trump. Approximately three percent of U.S. users across all examined age groups were registered to Truth Social.

In May 2022, an online survey in the United States found that only three percent of respondents were registered users of social media app Truth Social, launched in February 2022 by former U.S. President Donald Trump. Approximately four percent of U.S. male respondents reported having a Truth Social account, while only two percent of female respondents stated the same.

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 28 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, 5 percent of U.S. social media users say they like Truth Social. However, in actuality, among the 28 percent of U.S. respondents who know Truth Social, 18 percent of people like the brand.What is the usage share of Truth Social in the United States?All in all, 3 percent of social media users in the United States use Truth Social. That means, of the 28 percent who know the brand, 11 percent use them.How loyal are the users of Truth Social?Around 2 percent of social media users in the United States say they are likely to use Truth Social again. Set in relation to the 3 percent usage share of the brand, this means that 67 percent of their users show loyalty to the brand.What's the buzz around Truth Social in the United States?In February 2024, about 5 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 28 percent who know the brand, that's 18 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.

In May 2022, an online survey in the United States found that registered Truth Social users among respondents with an annual household income between 50,0000 and 100, 000 U.S. dollars were four percent. Truth Social was launched in February 2022 by former U.S. President Donald Trump. In comparison, around two percent of respondents with a household income under 50,000 U.S. dollars reported having an account on the former President's social media app.

As of March 2022, a survey conducted among registered voters in the United States found that 61 percent of women stated they would not be using former President Donald Trump's newly released social media platform, Truth Social. Additionally, 50 percent of male respondents said they would also not be using Truth Social. Of respondents who said they would use the social platform a lot, ten percent were men and six percent were women.

In May 2022, an online survey in the United States found that only three percent of white and Hispanic respondents were registered users of social media app Truth Social. Truth Social was launched in February 2022 by former U.S. President Donald Trump. Approximately four percent of Black respondents were registered users of Truth Social.

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.

The widespread dissemination of misinformation on social media is a serious threat to global health. To a large extent, it is still unclear who actually shares health-related misinformation deliberately and accidentally. We conducted a large-scale online survey among 5,307 Facebook users in six sub-Saharan African countries, in which we collected information on sharing of fake news and truth discernment. We estimate the magnitude and determinants of deliberate and accidental sharing of misinformation related to three vaccines (HPV, polio, and COVID-19). In an OLS framework we relate the actual sharing of fake news to several socioeconomic characteristics (age, gender, employment status, education), social media consumption, personality factors and vaccine-related characteristics while controlling for country and vaccine-specific effects. We first show that actual sharing rates of fake news articles are substantially higher than those reported from developed countries and that most of the sharing occurs accidentally. Second, we reveal that the determinants of deliberate vs. accidental sharing differ. While deliberate sharing is related to being older and risk-loving, accidental sharing is associated with being older, male, and high levels of trust in institutions. Lastly, we demonstrate that the determinants of sharing differ by the adopted measure (intentions vs. actual sharing) which underscores the limitations of commonly used intention-based measures to derive insights about actual fake news sharing behaviour.

Compared to applications that trigger massive information streams, like earthquakes and human disease epidemics, the data input for agricultural and environmental biosecurity events (ie. the introduction of unwanted exotic pests and pathogens), is expected to be sparse and less frequent. To investigate if Twitter data can be useful for the detection and monitoring of biosecurity events, we adopted a three-step process. First, we confirmed that sightings of two migratory species, the Bogong moth (Agrotis infusa) and the Common Koel (Eudynamys scolopaceus) are reported on Twitter. Second, we developed search queries to extract the relevant tweets for these species. The queries were based on either the taxonomic name, common name or keywords that are frequently used to describe the species (symptomatic or syndromic). Third, we validated the results using ground truth data. Our results indicate that the common name queries provided a reasonable number of tweets that were related to the ground truth data. The taxonomic query resulted in too small datasets, while the symptomatic queries resulted in large datasets, but with highly variable signal-to-noise ratios. No clear relationship was observed between the tweets from the symptomatic queries and the ground truth data. Comparing the results for the two species showed that the level of familiarity with the species plays a major role. The more familiar the species, the more stable and reliable the Twitter data. This clearly presents a problem for using social media to detect the arrival of an exotic organism of biosecurity concern for which public is unfamiliar.

TASK

With irony, language is employed in a figurative and subtle way to mean the opposite to what is literally stated. In case of sarcasm, a more aggressive type of irony, the intent is to mock or scorn a victim without excluding the possibility to hurt. Stereotypes are often used, especially in discussions about controversial issues such as immigration or sexism and misogyny. At PAN’22, we will focus on profiling ironic authors in Twitter. Special emphasis will be given to those authors that employ irony to spread stereotypes, for instance, towards women or the LGTB community. The goal will be to classify authors as ironic or not depending on their number of tweets with ironic content. Among those authors we will consider a subset that employs irony to convey stereotypes in order to investigate if state-of-the-art models are able to distinguish also these cases. Therefore, given authors of Twitter together with their tweets, the goal will be to profile those authors that can be considered as ironic.

DATA

Input

The uncompressed dataset consists in a folder which contains:

• A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id.
• A truth.txt file with the list of authors and the ground truth.

The format of the XML files is:

The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.

  2d0d4d7064787300c111033e1d2270cc:::I
  b9eccce7b46cc0b951f6983cc06ebb8:::NI
  f41251b3d64d13ae244dc49d8886cf07:::I
  47c980972060055d7f5495a5ba3428dc:::NI
  d8ed8de45b73bbcf426cdc9209e4bfbc:::I
  2746a9bf36400367b63c925886bc0683:::NI
  ...

Evaluation

The performance of your system will be ranked by accuracy.

More info on the task: https://pan.webis.de/clef22/pan22-web/author-profiling.html

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.

Datos utilizados para la elaboración del artículo
Análisis de las publicaciones con mayor repercusión en Facebook de los fact-checkers iberoamericanos en 2021