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.

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

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.

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

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

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

Youtube social network and ground-truth communities Dataset information 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.

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

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.

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.

Population-level national networks on social media are precious and essential for network science and behavioural science. This study proposes a data collection strategy for scraping online social networks at the population level, and thereby serving as a “ground truth” for the validation of both ego-centric and socio-centric data collection approaches. We proposed a set of validation approaches to evaluate the validity of our approach. Finally, we re-examined classical network and communication propositions (e.g., 80/20 rule, six degrees of separation) on the national network. Our proposed strategy would largely flourish the data collection pool of population-level social networks and further develop the research of network analysis in digital media environment.

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...

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 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-youtube.top5000.cmty.txt.gz file.

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 code and data to analyze the HKS Misinformation Review paper, "Cognitive reflection is associated with greater truth discernment for COVID-19 headlines, less trust but greater use of formal information sources, and greater willingness to pay for masks among social media users in Pakistan"

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.

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.

What accounts for the creation, design, and outputs of quasi-judicial institutions in autocracies? Prior research demonstrates that autocrats co-opt electoral, legislative, and judicial institutions to curtail opponents’ power and curry international patrons’ favor. However, scholarship on co-optation neglects quasi-judicial mechanisms, such as truth commissions, that can be useful for arranging a political narrative that bolsters a leader’s image while undermining his rivals. In this article, we formalize the concept of autocratic truth commissions—which account for one-third of truth commissions globally—and develop and test a novel theory of their origins, inputs, and outputs. We theorize that autocrats establish self-investigating commissions in response to threats to their symbolic authority and install rival-investigating commissions in response to threats to both symbolic authority and regime survival. We further argue that these two commission types take on different institutional forms and produce different outputs. Self-investigating commissions are afforded narrow mandates and produce reports that obscure basic facts. Meanwhile, rival-investigating commissions are granted wide mandates and culminate in accurate reports of rivals’ responsibility for abuses. We evaluate these expectations through comparative case studies of two autocratic truth commissions in Uganda, and find support.

A common approach to knowledge wh is to try to reduce it to knowledge that, and in particular to answer-knowledge. On this view, the truth- conditions of a knowledge wh ascription can be given entirely in terms of which answers to the embedded question the subject knows. Against this background, this paper considers the phenomenon of false-belief sensitivity — a challenge to this common approach to knowledge wh that has recently received a fair amount of attention in the question embedding literature. We present a series of experiments that help to bring the empirical standing of this phenomenon into focus. Across six experiments, our results provide evidence that truth judgments of knowledge wh ascriptions are affected by both the presence of false beliefs and the proportion of the subject’s beliefs that are false. Collectively, these results demonstrate a pattern of judgments that is incompatible with standard accounts of the relationship between knowledge that and knowledge wh, and with reducibility more generally. After presenting these new data, we end by considering the theoretical implications of this richer descriptive picture and outline a number of remaining open questions that should be pursued in future work.