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.

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.

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.

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.

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...

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 Truth Seeker Dataset is designed to support research in the detection and classification of misinformation on social media platforms, particularly focusing on Twitter. This dataset is part of a broader initiative to enhance the understanding of how machine learning (ML) and natural language processing (NLP) can be leveraged to identify fake news and misleading content in real-time.Dataset CompositionThe Truth Seeker Dataset comprises a substantial collection of social media posts that have been meticulously labeled as either real or fake. It was constructed using advanced ML algorithms and NLP techniques to analyze the language patterns in social media communications. The dataset includes:Raw Social Media Posts: A diverse range of tweets that reflect various topics and sentiments.Labeling: Each post is annotated with binary labels indicating its authenticity (real or fake).Feature Sets: Two distinct subsets of the dataset have been created using different NLP vectorization methods—Word2Vec and TF-IDF. This allows researchers to explore how different feature representations impact model performance.Research ApplicationsThe primary aim of the Truth Seeker Dataset is to facilitate the development and validation of models that can accurately classify social media content. Key applications include:Fake News Detection: Utilizing various ML algorithms, including Random Forest and AdBoost, which have demonstrated high F1 scores in preliminary evaluations.Model Comparison: Researchers can compare the effectiveness of different ML approaches on the same dataset, enabling a clearer understanding of which methods yield the best results in detecting misinformation.Algorithm Development: The dataset serves as a benchmark for developing new algorithms aimed at improving accuracy in fake news detection.

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.

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.

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 13 separate recordings in 3 variations: One obstacle" - features one obstacle in the environment and no robotMoving robot" - features one obstacle in the environment and the moving robot ``Three obstacles" - features three obstacles in the environment and no robot THOR - people tracks is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation. The data are available in three formats: mat - Matlab binary file TSV - text file bag - ROS bag file MAT files File - [char] Path to original QTM file Timestamp - [string] Date and time of the startof the data collection Start Fram - [char] 1 Frames - [double] Number of frames in the file FrameRate - [double] Number of frames per second Events - [struct] 0 Trajectories - [struct] 3D postion of observed reflective markers Labeled - [struct] Markers belonging to the tracked agents: Count - [double] Number of tracked markers Labels - [cell] List of marker labels Data - [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue RigidBodies - [struct] 6D pose of the helmet, corresponds to head poistion and orientation: Bodies - [double] Number of tracked bodies Name - [cell] Bodies Names Positions - [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body Rotations - [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body RPYs - [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles Residual - [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body TSV files 3D data File Header NO_OF_FRAMES - number of frames in the file NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz] NO_OF_ANALOG - number of analog inputs ANALOG_FREQUENCY - frequency of analog input DESCRIPTION - -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included MARKER_NAMES - names of tracked makers Column names Frame - frame ID Time - frame timestamp [marker name] [C] - coordinate of a [marker name] along [C] axis 6D data File Header NO_OF_FRAMES - number of frames in the file NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz] NO_OF_ANALOG - number of analog inputs ANALOG_FREQUENCY - frequency of analog input DESCRIPTION - -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included BODY_NAMES - names of tracked rigid bodies Colum Names Frame - frame ID Time - frame timestamp The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix 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.

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.

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.

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

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

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.

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"

THIS IS NO LONGER SUPPORTED. In ICEWS, an Event of Interest (EOI) is a macro-level occurrence within a country or region that is supported by the existence of multiple underlying events. The Ground Truth Data Set is a collection of data which lists, for the EOIs supported, whether or not the EOI did occur in any given country for any given month, historically speaking. We plan to update this data on a periodic basis. The five EOIs that are currently supported in this data set include: 1. Domestic Political Crisis (DPC): Significant opposition to the government, but not to the level of rebellion or insurgency (e.g., power struggles between two political factions involving disruptive strikes or violent clashes between supporters). 2. Insurgency: Organized opposition whose objective is to overthrow the central government. 3. International Crisis: Conflict or elevated tensions that could lead to conflict between two or more states OR between a state and an actor operating primarily from beyond the state's borders that involves the deployment of substantial ground forces (1,000+) beyond its borders. 4. Rebellion: Organized, active, violent opposition with substantial arms, where the objective is to seek autonomy or independence from the central government. 5. Ethnic/Religious Violence: Violence between ethnic or religious groups that is not specifically directed against the government. Additional information about the IC EWS program can be found at http://www.icews.com/. Follow our Twitter handle for data updates and other news: @icews