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.

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.

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.

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.

com-LiveJournal: LiveJournal social network and ground-truth communities

LiveJournal 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. As for the network, we provide the largest connected component.

com-Friendster: Friendster social network and ground-truth communities

Friendster is an on-line gaming network. Before re-launching as a game website, Friendster was a social networking site where users can form friendship edge each other. Friendster social network also allows users form a group which other members can then join. We consider such user-defined groups as ground-truth communities. For the social network, we take the induced subgraph of the nodes that either belong to at least one community or are connected to other nodes that belong to at least one community. This data is provided by The Web Archive Project, where the full graph is available.

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. As for the network, we provide the largest connected component.

com-Orkut: Orkut social network and ground-truth communities

Orkut 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.

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. As for the network, we provide the largest connected component.

com-Youtube: Youtube social network and ground-truth communities

Youtube 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.

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. As for the network, we provide the largest connected component.

com-DBLP: DBLP collaboration network and ground-truth communities

The DBLP computer science bibliography provides a comprehensive list of research papers in computer science. We construct a co-authorship network where two authors are connected if they publish at least one paper together. Publication venue, e.g, journal or conference, defines an individual ground-truth community; authors who published to a certain journal or conference form a community.

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. As for the network, we provide the largest connected component.

com-Amazon: Amazon product co-purchasing network and ground-truth communities

Network was collected by crawling Amazon website. It is based on Customers Who Bought This Item Also Bought feature of the Amazon website. If a product i is frequently co-purchased with product j, the graph contains an undirected edge from i to j. Each product category provided by Amazon defines each ground-truth community.

We regard each connected component in a product category 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. As for the network, we provide the largest connected component.

email-Eu-core: email-Eu-core network

The network was generated using email data from a large European research institution. We have anonymized information about all incoming and outgoing email between members of the research institution. Th...

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.

The dataset includes 9 separate recordings in 3 variations:

• ``One obstacle" - features one obstacle in the environment and no robot
• ``Moving robot" - features one obstacle in the environment and the moving robot
• ``Three obstacles" - features three obstacles in the environment and no robot

THOR - point clouds is the part of THÖR data set containing bag files with 3D scans collcted during the experiments.

Reference:

For more details check project website thor.oru.se or check our publications:

@article{thorDataset2019,
title={TH\"OR: Human-Robot Indoor Navigation Experiment
and Accurate Motion Trajectories Dataset},
author={Andrey Rudenko and Tomasz P. Kucner and
Chittaranjan S. Swaminathan and Ravi T. Chadalavada
and Kai O. Arras and Achim J. Lilienthal},
journal={arXiv preprint arXiv:1909.04403},
year={2019}
}

The real dataset consists of movie evaluations from IMDB, which provides a platform where individuals can evaluate movies on a scale of 1 to 10. If a user rates a movie and clicks the share button, a Twitter message is generated. We then extract the rating from the Twitter message. We treat the ratings on the IMDB website as the event truths, which are based on the aggregated evaluations from all users, whereas our observations come from only a subset of users who share their ratings on Twitter. Using the Twitter API, we collect information about the follower and following relationships between individuals that generate movie evaluation Twitter messages. To better show the influence of social network information on event truth discovery, we delete small subnetworks that consist of less than 5 agents. The final dataset we use consists of 2266 evaluations from 209 individuals on 245 movies (events) and also the social network between these 209 individuals. We regard the social network to be undirected as both follower or following relationships indicate that the two users have similar taste.

Dataset

This dataset was created by Clifford Hollins

Released under Apache 2.0

Contents

In this paper, we show that the propensity to agree with a given conspiracy theory depends conditionally on prior knowledge of that theory. First, we present our argument, drawing from results in psychology regarding the “illusion of truth” effect, which identifies the tendency of individuals to believe information based on exposure to that information alone. Then, we model the determinants of theory awareness, investigating which sub-populations are more likely to demonstrate theory familiarity. Lastly, we assess the conditional effect of familiarity on public opinion. Our results indicate that “hearing” and “believing” are linked: after controlling for salient observables, we find that the “illusion of truth” effect appears to be present in the formation of public opinion on conspiracy theories, but in a heterogeneous manner. In particular, there appear to be distinct mechanisms at work for the few theories that are familiar to more than 50 percent of respondents.

People tend to be bad at explicitly detecting lies. However, indirect veracity judgments and physiological responses may yield above-chance levels of accuracy in differentiating lies from the truth. If lies induce a threat response, vasoconstriction should reduce peripheral cutaneous blood flow, leading to finger temperature drops when confronted with a lie compared to the truth. Participants (N = 95) observed people telling lies or the truth about their social relationships, during which participants’ fingertip temperature was recorded via infrared thermal imaging. Results suggested that the accuracy of explicit veracity categorizations remained at chance levels. Judgments of story-tellers’ likability and trustworthiness as indirect veracity measures, as well as observers’ fingertip temperatures as a physiological veracity measure significantly differed between lies and true stories. However, the effects pointed in the opposite direction of our expectations: participants liked liars better than truth-tellers and trusted liars more; and fingertip temperatures increased while confronted with lies compared to true stories. We discuss that studying observers’ physiological responses may be a useful window to lie detection, but requires future investigation.

In recent years, concerns about misinformation in the media have skyrocketed. President Donald Trump has repeatedly claimed that various news outlets are disseminating "fake news" for political purposes. But when the information contained in mainstream media news reports provides no clear clues about its truth value nor any indication of a partisan slant, do people rely on the congeniality of the news outlet to judge whether the information is true or false? In a survey experiment, we presented partisans (Democrats and Republicans) and ideologues (liberals and conservatives) with a news article excerpt that varied by source shown (CNN, Fox News, or no source) and content (true or false information), and measured their perceived accuracy of the information contained in the article. Our results suggest that participants do not blindly judge the content of articles based on news source, regardless of their own partisanship and ideology. Contrary to prevailing views on the polarization and politicization of news outlets, as well as on voters' growing propensity to engage in "partisan motivated reasoning," source cues are not as important as the information itself for partisans on both sides of the aisle.

Since 1970, scores of states have established truth commissions to document political violence. Despite their prevalence and potential consequence, the question of why commissions are adopted in some contexts, but not in others, is not well understood. Relatedly, little is known about why some commissions possess strong investigative powers while others do not. I argue that the answer to both questions lies with domestic and international civil society actors, who are connected by a global transitional justice (TJ) network and who share the burden of guiding commission adoption and design. I propose that commissions are more likely to be adopted where network members can leverage information and moral authority over governments. I also suggest that commissions are more likely to possess strong powers where international experts, who steward TJ best practices, advise governments. I evaluate these expectations by analyzing two datasets in the novel Varieties of Truth Commissions Project, interviews with representatives from international non-governmental organizations, interviews with Guatemalan non-governmental organization leaders, a focus group with Argentinian human rights advocates, and a focus group at the International Center for Transitional Justice. My results indicate that network members share the burden—domestic members are essential to commission adoption, while international members are important for strong commission design.

This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. Our dataset not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is suitable for several research fields, such as work psychology, user modeling and context aware systems.The collection of this dataset was supported by the Dutch national program COMMIT (project P7 SWELL). SWELL is an acronym of Smart Reasoning Systems for Well-being at Work and at Home. Notes on the content of the dataset:- The uLog XML files refer to documents in the dataset. Most extensions of these files have changed due to file conversions. The original extension is now included in the file names at the end.- Due to copyrights not all original documents and images are included in the dataset.- Variable C in 'D - Physiology features (HR_HRV_SCL - final).csv' refers to the type of block, 1, 2 or 3.

Context

As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire. Such misinformation has caused confusion among people, disruptions in society, and even deadly consequences in health problems. To be able to understand, detect, and mitigate such COVID-19 misinformation, therefore, has not only deep intellectual values but also huge societal impacts. To help researchers combat COVID-19 health misinformation, this dataset created.

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https://img.etimg.com/thumb/msid-65836641,width-640,resizemode-4,imgsize-272192/fake-news.jpg" width="700">

Content

The datasets is a diverse COVID-19 healthcare misinformation dataset, including fake news on websites and social platforms, along with users' social engagement about such news. It includes 4,251 news, 296,000 related user engagements, 926 social platform posts about COVID-19, and ground truth labels.

• Version 0.1 (05/17/2020) initial version corresponding to arXiv paper CoAID: COVID-19 HEALTHCARE MISINFORMATION DATASET

• Version 0.2 (08/03/2020) added data from May 1, 2020 through July 1, 2020

• Version 0.3 (11/03/2020) added data from July 1, 2020 through September 1, 2020

Acknowledgements

Limeng Cui Dongwon Lee, Pennsylvania State University.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Digital World (DWACU) Stock: Truth Social's Ticket to the Top?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

A collection of 1 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

“Truth” has been used as a baseline condition in several functional magnetic resonance imaging (fMRI) studies of deception. However, like deception, telling the truth is an inherently social construct, which requires consideration of another person's mental state, a phenomenon known as Theory of Mind. Using a novel ecological paradigm, we examined blood oxygenation level dependent (BOLD) responses during social and simple truth telling. Participants (n = 27) were randomly divided into two competing teams. Post-competition, each participant was scanned while evaluating performances from in-group and out-group members. Participants were asked to be honest and were told that their evaluations would be made public. We found increased BOLD responses in the medial prefrontal cortex, bilateral anterior insula and precuneus when participants were asked to tell social truths compared to simple truths about another person. At the behavioral level, participants were slower at responding to social compared to simple questions about another person. These findings suggest that telling the truth is a nuanced cognitive operation that is dependent on the degree of mentalizing. Importantly, we show that the cortical regions engaged by truth telling show a distinct pattern when the task requires social reasoning.

Online news can easily deceive even the most vigilant individuals. Take Deepa, for example: She received a notification on her phone announcing a week-long holiday declared by the government due to an abnormal cold wave. Quickly, she shared the news with her colleagues and friends, only to realize later it was false—a result of a disinformation campaign circulating on social media. It is often difficult for lay persons to distinguish between fact and disinformation or misinformation. While journalists must make this crucial distinction, they need the necessary skills and tools. This is particularly true in smaller countries with limited training opportunities and exposure to international best practices. In this context, in December 2023 Canada’s Embassy in Nepal hosted two capacity building workshops for journalists and students of mass communications in Kathmandu. The Embassy partnered with Kathmandu University – a prominent autonomous university in Nepal that is dedicated to maintaining academic excellence, and with Social Media Matters, a well-known organization which promotes online safety.

Proponents of the social contract theory of morality often claim that social contract is tacit or implicit. However, the question is what does it exactly mean? In my opinion, we should understand it in the sense that social contract theory is true of individuals a, b, c, ... and norm Φ iff: (1) a, b, c, ... behave according to Φ , (2) each one of them does so because he/she believes that each one of them will be better off if all of them behave according to Φ , (3) each one of them expects others will behave according to Φ for the same reason. When these three conditions are satisfied, we have to say that a, b, c, ... behave as if they agreed to do Φ (in the relevant sense), even if no explicit deal has been made. This definition is useful for several further purposes ; it enables us to (a) delineate morality - moral norms are exactly those norms which satisfy these conditions ; (b) define prejudices as norms which are falsely believed to make us better off, that is, falsely believed to satisfy condition (2) ; (c) define moral erosion as a proces of weakening conditions (1) and (3), often in spite of true belief that we would be better off if we all accept Φ , (d) testing the truth of the social contract theory ; if all of our basic moral intuitions (or at least great majority) satisfy these conditions, we should regard it as a true theory.