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.

Dataset

This dataset was created by Tom Hastie

Released under CC0: Public Domain

Contents

It contains the following files:

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}
}

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} }

Dataset

This dataset was created by Vivian

Contents

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

This data is scrapped from the Politifact website. It contains the claims made by individuals and what does the Politifact curators think about the same. This data can be used in order to run various NLP algorithms in order to find the integrity of the data and also determining the validity of a claim.

Content

Image for associating the content:- When you land on Politifact website. You will see the page with the list of facts as shown below. I have also annotated the various column fields with the image for convenience.

https://i.imgur.com/9MH52Uf.jpg" alt="Landing page for fact check page of Politifact">

Now when you click the article you land on the main page and the annotation for the curator is on the main page. You can see it as follows:- https://i.imgur.com/c9Ht0fp.jpg" alt="Article and other info">

The content of the data is scrapped from the Politifact site and has various attributes. This list of attributes are covered below:- - sources: String representing the person who is associated with the quote. - sources_dates: Date on which the information was furnished by the source. - sources_post_location: The location/medium at which the source furnished the information. - sources_quote: The actual quote/information furnished by the source in question. - curator_name: Person who curated the information from the source. - curated_date:Date at which the curator analyzed and assessed the source's quote. - fact: Fact score that is assigned to the source's quote. - sources_url: URL of the curator's article about the source's quote - curators_article_title: Title of the article written by the curator to support/reject the source's claim - curator_complete_article: Complete blog written by the curator supporting/rejecting the source's claim - curator_tags: Tags given by curator to the blog post.

Acknowledgements

The entire acknowledgment goes to Politifact.com for curating and validating such data and facts.

Dataset Card for Polarization GameNet

  Dataset Summary

Polarization GameNet is a benchmark dataset for modeling two-player zero-sum polarization games in social networks. Each data sample includes a synthetic social graph, node-level opinion states, and the ground truth Nash equilibrium strategies for both players, computed using a game-theoretic solver. The Nash equilibrium arises from a strategic interaction between two agents—one aiming to maximize network polarization… See the full description on the dataset page: https://huggingface.co/datasets/huggingfacexzhan176/Social-Network-Polarization-Game-Nash-Equilibrium.

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.

Content: attitudes towards religious practices.Topics: assessment of personal happiness; attitudes towards pre-maritalsexual intercourse; attitudes towards committed adultery; attitudestowards homosexual relationships between adults; attitudes towardsabortion in case of serious disability or illness of the baby or lowincome of the family; attitudes towards gender roles in marriage; trustin institutions (parliament, business and industry, churches andreligious organizations, courts and the legal system, schools and theeducational system); mobility; attitudes towards the influence ofreligious leaders on voters and government; attitudes towards thebenefits of science and religion (scale: modern science does more harmthan good, too much trust in science and not enough in religious faith,religions bring more conflicts than peace, intolerance of people withvery strong religious beliefs); judgement on the power of churches andreligious organizations; attitudes towards equal rights for allreligious groups in the country and respect for all religions;acceptance of persons from a different religion or with differentreligious views in case of marrying a relative or being a candidate ofthe preferred political party (social distance); attitudes towards theallowance for religious extremists to hold public meetings and topublish books expressing their views (freedom of expression); doubt orfirm belief in God (deism, scale); belief in: a life after death,heaven, hell, religious miracles, reincarnation, Nirvana, supernaturalpowers of deceased ancestors; attitudes towards a higher truth andtowards meaning of life (scale: God is concerned with every human beingpersonally, little that people can do to change the course of theirlives (fatalism), life is meaningful only because God exists, life doesnot serve any purpose, life is only meaningful if someone provides themeaning himself, connection with God without churches or religiousservices); religious preference (affiliation) of mother, father andspouse/partner; religion respondent was raised in; frequency ofchurch attendance (of attendance in religious services) of father andmother; personal frequency of church attendance when young; frequencyof prayers and participation in religious activities; shrine, altar ora religious object in respondent’s home; frequency of visiting a holyplace (shrine, temple, church or mosque) for religious reasons exceptregular religious services; self-classification of personalreligiousness and spirituality; truth in one or in all religions;attitudes towards the profits of practicing a religion (scale: findinginner peace and happiness, making friends, gaining comfort in times oftrouble and sorrow, meeting the right kind of people).Optional items (not stated in all countries): questions in countrieswith an appreciable number of Evangelical Protestants): ´born-again´Christian; attitudes towards the Bible (or appropriate holy book);questions generally applicable for all countries: conversion of faithafter crucial experience; personal sacrifice as an expression of faithsuch as fasting or following a special diet during holy season such asLent or Ramadan; concept of God (semantic differential scale: mother -father, master - spouse, judge - lover, friend - king); belief in luckycharms, fortune tellers, faith healers and horoscopes; social rules orGod’s laws as basis for deciding between right and wrong; attitudestowards members of different religious groups (Christians, Muslims,Hindus, Buddhists, Jews, Atheists or non-believers.Demography: sex; age; marital status; steady life partner; years ofschooling; highest education level; country specific education anddegree; current employment status (respondent and partner); hoursworked weekly; occupation (ISCO 1988) (respondent and partner);supervising function at work; working for private or public sector orself-employed (respondent and partner); if self-employed: number ofemployees; trade union membership; earnings of respondent (countryspecific); family income (country specific); size of household;household composition; party affiliation (left-right); country specificparty affiliation; participation in last election; religiousdenomination; religious main groups; attendance of religious services;self-placement on a top-bottom scale; region (country specific); sizeof community (country specific); type of community: urban-rural area;country of origin or ethnic group affiliation.Additionally coded: administrative mode of data-collection; weightingfactor; case substitution. Einstellung zur religiösen Praxis.Themen: Einschätzung des persönlichen Glücksgefühls; Einstellung zuvorehelichem Geschlechtsverkehr und zu außerehelichemGeschlechtsverkehr (Ehebruch); Einstellung zu homosexuellen Beziehungenzwischen Erwachsenen; Einstellung zu Abtreibung im Falle vonBehinderung oder Krankheit des Babys und im Falle geringen Einkommensder Familie; Rollenverständnis in der Ehe; Institutionenvertrauen(Parlament, Unternehmen und Industrie, Kirche und religiöseOrganisationen, Gerichte und Rechtssystem, Schulen und Bildungssystem);eigene Mobilität; Einstellung zum Einfluss von religiösen Führern aufWähler und Regierung; Einstellung zu Wissenschaft und Religion (Skala:moderne Wissenschaft bringt mehr Schaden als Nutzen, zu viel Vertrauenin die Wissenschaft und zu wenig religiöses Vertrauen, Religionenbringen mehr Konflikte als Frieden, Intoleranz von Menschen mit starkenreligiösen Überzeugungen); Beurteilung der Macht von Kirchen undreligiösen Organisationen im Lande; Einstellung zur Gleichberechtigungaller religiösen Gruppen im Land und Respekt für alle Religionen;Akzeptanz einer Person anderen Glaubens oder mit unterschiedlichenreligiösen Ansichten als Ehepartner im Verwandtschaftskreis sowie alsKandidat der präferierten Partei (soziale Distanz); Einstellung zuröffentlichen Redefreiheit bzw. zum Publikationsrecht für religiöseExtremisten; Zweifel oder fester Glaube an Gott (Skala Deismus); Glaubean: ein Leben nach dem Tod, Himmel, Hölle, Wunder, Reinkarnation,Nirwana, übernatürliche Kräfte verstorbener Vorfahren; Einstellung zueiner höheren Wahrheit und zum Sinn des Lebens (Gott kümmert sich umjeden Menschen persönlich, nur wenig persönlicher Einfluss auf dasLeben möglich (Fatalismus), Leben hat nur einen Sinn aufgrund derExistenz Gottes, Leben dient keinem Zweck, eigenes Tun verleiht demLeben Sinn, persönliche Verbindung mit Gott ohne Kirche oderGottesdienste); Religion der Mutter, des Vaters und des Ehepartnersbzw. Partners; Religion, mit der der Befragte aufgewachsen ist;Kirchgangshäufigkeit des Vaters und der Mutter; persönlicheKirchgangshäufigkeit in der Jugend; Häufigkeit des Betens und derTeilnahme an religiösen Aktivitäten; Schrein, Altar oder religiösesObjekt (z.B. Kreuz) im Haushalt des Befragten; Häufigkeit des Besuchseines heiligen Ortes (Schrein, Tempel, Kirche oder Moschee) ausreligiösen Gründen; Selbsteinschätzung der Religiosität undSpiritualität; Wahrheit in einer oder in allen Religionen;Vorteilhaftigkeit der Ausübung einer Religion (Skala: inneren Friedenund Glück finden, Freundschaften schließen, Unterstützung inschwierigen Zeiten, Gleichgesinnte treffen).Optionale Items (nicht in allen Ländern ausgeführt): Fragen in Ländernmit einer bedeutenden Anzahl evangelikaler Protestanten: wiedergeboreneChristen; Einstellung zur Bibel; Fragen, die grundsätzlich für alleLänder anwendbar sind: Bekehrung zum Glauben nach einemSchlüsselerlebnis; persönliche Opfer als Ausdruck des Glaubens wieFasten oder Einhalten einer speziellen Diät während heiliger Zeiten wiez.B. Ramadan; Konzept von Gott (semantisches Differential:Mutter/Vater, Herr und Meister/Ehepartner, Richter/Liebender,Freund/König); Glaube an Glücksbringer, Wahrsager, Gesundbeter undHoroskope; demokratische oder göttliche Gesetze als Grundlage fürEntscheidungen zwischen richtig und falsch; Einstellung gegenüberverschiedenen religiösen Gruppen (Christen, Muslime, Hindus,Buddhisten, Juden, Atheisten oder Nicht-Gläubige).Demographie: Geschlecht; Alter; Familienstand; Zusammenleben mit einemPartner; Jahre der Schulbildung, höchster Bildungsabschluss;länderspezifischer Bildungsgrad; derzeitiger Beschäftigungsstatus desBefragten und seines Partners; Beruf (ISCO-88) des Befragten und seinesPartners; Vorgesetztenfunktion; Beschäftigung im privaten oderöffentlichen Dienst oder Selbständigkeit des Befragten und seinesPartners; Selbständige wurden gefragt: Anzahl der Beschäftigten;Gewerkschaftsmitgliedschaft; Einkommensquellen des Befragten(länderspezifisch), Haushaltseinkommen (länderspezifisch);Haushaltsgröße; Haushaltszusammensetzung; Parteipräferenz(links-rechts), länderspezifische Parteipräferenz; Wahlbeteiligung beider letzten Wahl; Konfession; Kirchgangshäufigkeit; Selbsteinstufungauf einer Oben-Unten-Skala; Region und Ortsgröße (länderspezifisch),Urbanisierungsgrad; Geburtsland und ethnische Herkunft.Zusätzlich verkodet wurde: Datenerhebungsart; Gewichtungsfaktoren.

The EduAffect Dataset is a novel dataset designed specifically for the Academic and Social realities of University Students. It captures the Affect and Cognitive states that are critical predictors of Student success and engagement. The dataset provides a ground-truth lens into Student experiences, enabling the development of AI that is not only Smart but also Empathetic.

Truth Detection / Lie Detection / Deception Detection / Fact Checking

Context

The following dataset was obtained by parsing statements and their veracity verdict from Politifact.com. Contains 14k affirmations up till late 2020.

The statements obtained are of 6 categories: True, Mostly True, Half-True, Mostly False, False, Pants on Fire!

This dataset can be used for multiple purposes: attempting to detect truthfulness based on statement language (or conversely, detecting lies), fact-checking integration or just EDA for political purposes.

Content

There are 4 columns in politifact.csv: statement, source, link, veracity.

statement - statement made by celebrity or politician. source - can be a person, but not necessarily. link - URL of affirmation. veracity - degree of truthfulness given by the Politifact.com team.

Other variants have certain classes removed and are binarized (into truths and lies). Have a quick look over this notebook for more details: https://www.kaggle.com/thesergiu/part-1-quick-eda-on-politifact-csv

Don't forget to upvote the dataset if you find it useful!

Acknowledgements

Initial Source: www.politifact.com Creator GitHub Link: https://github.com/the-sergiu GitHub Repo Link for more context: https://github.com/the-sergiu/TruthDetection

CausaLM Datasets

When evaluating a trained classification model, we usually have access to a test-set, consisting of manually labeled examples that the model was not trained on, and can hence be used for evaluation. Estimating causal effects is often harder in comparison, as we do not have access to the ground truth.

To allow for ground-truth comparisons and to spur further research on causal inference in NLP, we propose here four cases where causal effects can be estimated. In three out of those cases (adjectives, gender and race), we have constructed datasets with counterfactual examples so that the causal estimators can be compared to the ground truth.

Dataset

This dataset was created by Clifford Hollins

Released under Apache 2.0

Contents

Context Extensive research has focused on using data and satellite imagery in precision agriculture, but its use in rangeland management remains underexplored. Rangelands, covering 70% of the world’s land and supporting over 500 million people worldwide, are key for food security and the economy in some of the harshest environments. Unfortunately, they are increasingly threatened by climate change. Nearly half of the world’s rangelands face degradation, with severe impacts on biodiversity, food security, and economies, particularly in arid regions like Namibia, where 80% of the population relies on rangelands. Lack of sustainable practices aggravates this issue, as does bush encroachment, a phenomenon characterised by the increase of shrub species at the expense of palatable grasses. Namibia suffers from bush encroachment on a massive scale, with over 80% of its land affected by this issue. Accurate data is critical for sustainable rangeland management, enabling predictions of productivity, drought planning, and optimal stocking rates. However, data collection in Namibia and the SADC region remains limited despite abundant satellite imagery. To address this gap, Farm4Trade, in collaboration with the University of Namibia, Omeva Consulting, and local experts, has created a dataset. This achievement was made possible through the sponsorship and support of the Lacuna Fund, the world’s first collaborative initiative dedicated to empowering data scientists, researchers, and social entrepreneurs in low- and middle-income regions with the resources needed to create labelled datasets that tackle pressing local challenges.

Content The dataset comprises comprehensive field data collected from 20 sites across Namibia's major livestock-producing ecoregions, with observations conducted during four rounds in February 2023, May 2023, February 2024, and April 2024. The data includes: • Digitized Excel Sheets: Ground truth data, including green coverage, biomass quantity, standing herbaceous crop and grazing patterns. • Plot Pictures: Photographs captured during data collection rounds. • Other data: plot biomass and dominant species identification • Supporting Information: o Maps: Visual representations of the 20 study sites and their distribution across Namibia’s ecoregions. o Coordinates: a table with the exact coordinates of the sites, plots ecoregions where these falls as well as dominant species in the area. o Supportive Manual: Comprehensive guidance on data collection methodologies, digitization processes, and case studies. These includes two practical examples demonstrating the application of the dataset for rangeland management. o API: A tool for integrating the dataset with satellite imagery, allowing for custom geospatial analyses.

Acknowledgements We extend our sincere appreciation to the University of Namibia for providing field sites for data collection, as well as for assigning dedicated students and researchers to support the project. We are particularly grateful to Dr. Axel Rothauge of Agriconsult Namibia and Dr. Cornelis Van der Waal of Agriecological Services for their invaluable expertise in developing the field methodology and for their guidance in training and overseeing the data collection process.

We also wish to thank Tangeni Nakapipi for his expertise in creating the project’s maps. Additionally, we extend our gratitude to the interns from ArewaLadies4Tech, whose efforts in testing the dataset have been invaluable. Most importantly, we would like to thank the field enumerators for their hard work and meticulous attention to detail, which has made this dataset possible. We are also deeply grateful to the farmers for their kindness in allowing data collection on their farms.

We would like to express our appreciation to the funders. This dataset and manual have been made possible through support from Lacuna Fund, the world’s first collaborative initiative dedicated to empowering data scientists, researchers, and social entrepreneurs in low- and middle-income regions with the resources necessary to create labelled datasets that address pressing local challenges. Lacuna Fund is a collaborative funding effort supported by The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as the implementing agency, Wellcome Trust, the Gordon and Betty Moore Foundation, the Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. More information about Lacuna Fund can be found at https://lacunafund.org/about/.