In April 2024, Truth Social saw a total of 3.9 million desktop and mobile web visits in the United States, down from 4.8 million in March 2024. Monthly desktop and mobile web visits of the platform peaked in August 2022, reaching 9.8 million visits. Truth Social is an American media and technology company owned by former U.S. president Donald Trump.

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.

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.

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 latest hot topic in the news is fake news and many are wondering what data scientists can do to detect it and stymie its viral spread. This dataset is only a first step in understanding and tackling this problem. It contains text and metadata scraped from 244 websites tagged as "bullshit" by the BS Detector Chrome Extension by Daniel Sieradski.

Warning: I did not modify the list of news sources from the BS Detector so as not to introduce my (useless) layer of bias; I'm not an authority on fake news. There may be sources whose inclusion you disagree with. It's up to you to decide how to work with the data and how you might contribute to "improving it". The labels of "bs" and "junksci", etc. do not constitute capital "t" Truth. If there are other sources you would like to include, start a discussion. If there are sources you believe should not be included, start a discussion or write a kernel analyzing the data. Or take the data and do something else productive with it. Kaggle's choice to host this dataset is not meant to express any particular political affiliation or intent.

Contents

The dataset contains text and metadata from 244 websites and represents 12,999 posts in total from the past 30 days. The data was pulled using the webhose.io API; because it's coming from their crawler, not all websites identified by the BS Detector are present in this dataset. Each website was labeled according to the BS Detector as documented here. Data sources that were missing a label were simply assigned a label of "bs". There are (ostensibly) no genuine, reliable, or trustworthy news sources represented in this dataset (so far), so don't trust anything you read.

Fake news in the news

For inspiration, I've included some (presumably non-fake) recent stories covering fake news in the news. This is a sensitive, nuanced topic and if there are other resources you'd like to see included here, please leave a suggestion. From defining fake, biased, and misleading news in the first place to deciding how to take action (a blacklist is not a good answer), there's a lot of information to consider beyond what can be neatly arranged in a CSV file.

• How Fake News Spreads (NYT)

• We Tracked Down A Fake-News Creator In The Suburbs. Here's What We Learned (NPR)

• Does Facebook Generate Over Half of its Revenue from Fake News? (Forbes)

• Fake News is Not the Only Problem (Points - Medium)

• Washington Post Disgracefully Promotes a McCarthyite Blacklist From a New, Hidden, and Very Shady Group (The Intercept)

Improvements

If you have suggestions for improvements or would like to contribute, please let me know. The most obvious extensions are to include data from "real" news sites and to address the bias in the current list. I'd be happy to include any contributions in future versions of the dataset.

Acknowledgements

Thanks to Anthony for pointing me to Daniel Sieradski's BS Detector. Thank you to Daniel Nouri for encouraging me to add a disclaimer to the dataset's page.

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} }

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.

The HumAID Twitter dataset consists of several thousands of manually annotated tweets that have been collected during nineteen major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened during 2016 to 2019 across different parts of the World. It is the largest social media dataset (~77K) for crisis informatics so far (for details please refer to our paper). The annotations consist of following humanitarian categories. Humanitarian categories Caution and advice Displaced people and evacuations Dont know cant judge Infrastructure and utility damage Injured or dead people Missing or found people Not humanitarian Other relevant information Requests or urgent needs Rescue volunteering or donation effort Sympathy and support Data format and directories =========================== The data directory contains the following three sub-directories: events/ This directory contains sub-directories for each event. In which each event directory contains tab-separated (i.e., TSV) three files, i.e., train, dev and test. Each TSV file stores ground-truth annotations for the aforementioned humanitarian categories. The data format of these files is described in detail below. event_type/ This directory contains combined event type data, we combined the training, development, and test sets of all the events that belong to the same event type. all_combined/ This directory contains the whole combined set. HumAID_ICWSM_data.jsonl: Json objects of tweets Format of the TSV files --------------------------------------------------------- Each TSV file contains the following columns, separated by a tab: tweet_id: corresponds to the actual tweet id from Twitter. tweet_text: corresponds to the tweet text. class_label: corresponds to a label assigned to a given tweet text. More details can also be found in: https://crisisnlp.qcri.org/humaid_dataset

It is increasingly common to observe that certain governments target academic institutions such as the Central European University in Hungary, Boğaziçi University in Turkey, or CIDE in Mexico. These attacks represent the most visible symptoms of the deterioration of academic freedom. What is the cause of this trend? We argue that populism, being a thin ideology that polarizes the public sphere into virtuous citizens and a corrupt elite while emphasizing the will of the people, has made universities and academics natural targets for leaders who seek to impose a narrative in which only they possess the truth and represent the will of the people. Universities are characterized by a pluralism of ideas, but also possess an elitist character; these attributes are in direct conflict with the values and vision of populist leaders. To support this argument, we present a global statistical analysis correlating the degree of populism exhibited by executive leaders with the extent of academic freedoms between 2000 and 2021, based on data from the Global Populism Database and V-Dem, and illustrate our arguments with an in-depth analysis of the case of CIDE in Mexico.

I experimentally investigate the hypothesis that many people avoid lying even in a situation where doing so would result in a Pareto improvement. Replicating (Erat and Gneezy, Management Science 58, 723-733, 2012), I find that a significant fraction of subjects tell the truth in a sender-receiver game where both subjects earn a higher payoff when the partner makes an incorrect guess regarding the roll of a die. However, a non-incentivized questionnaire indicates that the vast majority of these subjects expected their partner not to follow their message. I conduct two new experiments explicitly designed to test for a 'pure' aversion to lying, and find no evidence for the existence of such a motivation. I discuss the implications of the findings for moral behavior and rule following more generally.

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

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.

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.

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.

Einstellung zur religiösen Praxis. Themen: Einschätzung des persönlichen Glücksgefühls; Einstellung zu vorehelichem Geschlechtsverkehr und zu außerehelichem Geschlechtsverkehr (Ehebruch); Einstellung zu homosexuellen Beziehungen zwischen Erwachsenen; Einstellung zu Abtreibung im Falle von Behinderung oder Krankheit des Babys und im Falle geringen Einkommens der Familie; Rollenverständnis in der Ehe; Personenvertrauen vs. Vorsicht im Umgang mit Menschen; Institutionenvertrauen (Parlament, Unternehmen und Industrie, Kirche und religiöse Organisationen, Gerichte und Rechtssystem, Schulen und Bildungssystem); eigene Mobilität; Einstellung zum Einfluss von religiösen Führern auf Wähler und Regierung; Einstellung zu Wissenschaft und Religion (Skala: moderne Wissenschaft bringt mehr Schaden als Nutzen, zu viel Vertrauen in die Wissenschaft und zu wenig religiöses Vertrauen, Religionen bringen mehr Konflikte als Frieden, Intoleranz von Menschen mit starken religiösen Überzeugungen); Beurteilung der Macht von Kirchen und religiösen Organisationen im Lande; Einstellung zur Gleichberechtigung aller religiösen Gruppen im Land und Respekt für alle Religionen; Akzeptanz einer Person anderen Glaubens oder mit unterschiedlichen religiösen Ansichten als Ehepartner im Verwandtschaftskreis sowie als Kandidat der präferierten Partei (soziale Distanz); Einstellung zur öffentlichen Redefreiheit bzw. zum Publikationsrecht für religiöse Extremisten; Zweifel oder fester Glaube an Gott (Skala Deismus); Glaube an: ein Leben nach dem Tod, Himmel, Hölle, Wunder, Reinkarnation, Nirwana, übernatürliche Kräfte verstorbener Vorfahren; Einstellung zu einer höheren Wahrheit und zum Sinn des Lebens (Gott kümmert sich um jeden Menschen persönlich, nur wenig persönlicher Einfluss auf das Leben möglich (Fatalismus), Leben hat nur einen Sinn aufgrund der Existenz Gottes, Leben dient keinem Zweck, eigenes Tun verleiht dem Leben Sinn, persönliche Verbindung mit Gott ohne Kirche oder Gottesdienste); Religion der Mutter, des Vaters und des Ehepartners bzw. Partners; Religion, mit der der Befragte aufgewachsen ist; Kirchgangshäufigkeit des Vaters und der Mutter; persönliche Kirchgangshäufigkeit in der Jugend; Häufigkeit des Betens und der Teilnahme an religiösen Aktivitäten; Schrein, Altar oder religiöses Objekt (z.B. Kreuz) im Haushalt des Befragten; Häufigkeit des Besuchs eines heiligen Ortes (Schrein, Tempel, Kirche oder Moschee) aus religiösen Gründen; Selbsteinschätzung der Religiosität und Spiritualität; Wahrheit in einer oder in allen Religionen; Vorteilhaftigkeit der Ausübung einer Religion (Skala: inneren Frieden und Glück finden, Freundschaften schließen, Unterstützung in schwierigen Zeiten, Gleichgesinnte treffen). Optionale Items (nicht in allen Ländern ausgeführt): wiedergeborene Christen; Einstellung zur Bibel; Fragen, die grundsätzlich für alle Länder anwendbar sind: Bekehrung zum Glauben nach einem Schlüsselerlebnis; persönliche Opfer als Ausdruck des Glaubens wie Fasten oder Einhalten einer speziellen Diät während heiliger Zeiten wie z.B. Ramadan; Konzept von Gott (semantisches Differential: Mutter/Vater, Herr und Meister/Ehepartner, Richter/Liebender, Freund/König); Glaube an Glücksbringer, Wahrsager, Gesundbeter und Horoskope; Entscheidungskriterien für persönliches Handeln (Gesetzte oder religiöse Prinzipien); Einstellung gegenüber verschiedenen religiösen Gruppen (Christen, Muslime, Hindus, Buddhisten, Juden, Atheisten oder Nicht-Gläubige). Demographie: Geschlecht; Alter; Familienstand; Zusammenleben mit einem Partner; Jahre der Schulbildung, höchster Bildungsabschluss; länderspezifischer Bildungsgrad; derzeitiger Beschäftigungsstatus des Befragten und seines Partners; Wochenarbeitszeit; Beruf (ISCO-88) des Befragten und seines Partners; Vorgesetztenfunktion; Beschäftigung im privaten oder öffentlichen Dienst oder Selbständigkeit des Befragten und seines Partners; Selbständige wurden gefragt: Anzahl der Beschäftigten; Gewerkschaftsmitgliedschaft; Einkommensquellen des Befragten (länderspezifisch), Haushaltseinkommen (länderspezifisch); Haushaltsgröße; Haushaltszusammensetzung; Parteipräferenz (links-rechts), länderspezifische Parteipräferenz; Wahlbeteiligung bei der letzten Wahl; Konfession; Kirchgangshäufigkeit; Selbsteinstufung auf einer Oben-Unten-Skala; Region und Ortsgröße (länderspezifisch), Urbanisierungsgrad; Geburtsland und ethnische Herkunft. Zusätzlich verkodet wurde: Datenerhebungsart; Gewichtungsfaktoren; case substitution. Content: attitudes towards religious practices. Topics: assessment of personal happiness; attitudes towards pre-marital sexual intercourse; attitudes towards committed adultery; attitudes towards homosexual relationships between adults; attitudes towards abortion in case of serious disability or illness of the baby or low income of the family; attitudes towards gender roles in marriage; people can be trusted vs. can´t be too careful in dealing with people; trust in institutions (parliament, business and industry, churches and religious organizations, courts and the legal system, schools and the educational system); mobility; attitudes towards the influence of religious leaders on voters and government; attitudes towards the benefits of science and religion (scale: modern science does more harm than good, too much trust in science and not enough in religious faith, religions bring more conflicts than peace, intolerance of people with very strong religious beliefs); judgement on the power of churches and religious organizations; attitudes towards equal rights for all religious groups in the country and respect for all religions; acceptance of persons from a different religion or with different religious views in case of marrying a relative or being a candidate of the preferred political party (social distance); attitudes towards the allowance for religious extremists to hold public meetings and to publish books expressing their views (freedom of expression); doubt or firm belief in God (deism, scale); belief in: a life after death, heaven, hell, religious miracles, reincarnation, Nirvana, supernatural powers of deceased ancestors; attitudes towards a higher truth and towards meaning of life (scale: God is concerned with every human being personally, little that people can do to change the course of their lives (fatalism), life is meaningful only because God exists, life does not serve any purpose, life is only meaningful if someone provides the meaning himself, connection with God without churches or religious services); religious preference (affiliation) of mother, father and spouse/partner; religion respondent was raised in; frequency of church attendance (of attendance in religious services) of father and mother; personal frequency of church attendance when young; frequency of prayers and participation in religious activities; shrine, altar or a religious object in respondent’s home; frequency of visiting a holy place (shrine, temple, church or mosque) for religious reasons except regular religious services; self-classification of personal religiousness and spirituality; truth in one or in all religions; attitudes towards the profits of practicing a religion (scale: finding inner peace and happiness, making friends, gaining comfort in times of trouble and sorrow, meeting the right kind of people). Optional items (not stated in all countries): ´born-again´ Christian; attitudes towards the Bible (or appropriate holy book); questions generally applicable for all countries: conversion of faith after crucial experience; personal sacrifice as an expression of faith such as fasting or following a special diet during holy season such as Lent or Ramadan; concept of God (semantic differential scale: mother - father, master - spouse, judge - lover, friend - king); belief in lucky charms, fortune tellers, faith healers and horoscopes; decision criteria for personal actions (laws or religious principles); attitudes towards members of different religious groups (Christians, Muslims, Hindus, Buddhists, Jews, Atheists or non-believers). Demography: sex; age; marital status; steady life partner; years of schooling; highest education level; country specific education and degree; current employment status (respondent and partner); hours worked weekly; occupation (ISCO 1988) (respondent and partner); supervising function at work; working for private or public sector or self-employed (respondent and partner); if self-employed: number of employees; trade union membership; earnings of respondent (country specific); family income (country specific); size of household; household composition; party affiliation (left-right); country specific party affiliation; participation in last election; religious denomination; religious main groups; attendance of religious services; self-placement on a top-bottom scale; region (country specific); size of community (country specific); type of community: urban-rural area; country of origin or ethnic group affiliation. Additionally coded: administrative mode of data-collection; weighting factor; case substitution.

Einstellung zu religiösen Verhaltensweisen. Themen: Persönliche Glückseinschätzung; Einschätzung derVerantwortung des Staates bezüglich der Arbeitsbeschaffung undder Einkommensnivellierung; Einstellung zum vorehelichen sowie zumaußerehelichen Geschlechtsverkehr; Einstellung zur Homosexualität undzur Abtreibung; Beurteilung der Rollenverteilung in der Ehe undEinstellung zu berufstätigen Frauen; Einstellung zum Zusammenleben miteinem Partner vor der Ehe auch ohne Heiratsabsicht; Steuerehrlichkeitund Einstellung zur Ehrlichkeit des Bürgers gegenüber dem Staat;Vertrauen in andere Menschen sowie in Institutionen wie Parlament,Wirtschaft, Industrie, Kirchen, Gerichte und Schulen; Einstellung zurEinflußnahme von Kirchenführern auf Wähler und Regierungen; Einstellungzur Nutzenstiftung moderner Wissenschaft; größeres Vertrauen in dieWissenschaft als in die Religion; mehr Konflikte statt Frieden durch dieReligionen; Intoleranz streng gläubiger Menschen; zu viel Einfluß derReligion im eigenen Land; Häufigkeit eigener ehrenamtlicher Tätigkeitenim letzten Jahr in politischen, karitativen, religiösen oder anderenOrganisationen; Beurteilung der Macht von Kirchen und religiösenOrganisationen; Zweifel oder fester Gottesglaube; empfundene Nähe zuGott; Glauben an ein Leben nach dem Tod, den Himmel, die Hölle und anWunder; Einstellung zur Bibel; Gott befaßt sich mit jedem Menschen;Fatalismus; Sinn des Lebens und christliche Lebensdeutung; religiöseBindung an einen Wendepunkt im Leben; Religionszugehörigkeit des Vaters,der Mutter und des (Ehe)-Partners; Kirchgangshäufigkeit des Vaters undder Mutter; eigene Glaubensrichtung und Kirchgangshäufigkeit imJugendalter; Häufigkeit des Betens und der Teilnahme an religiösenAktivitäten; Selbsteinstufung eigener Religiosität; Wahrheit in eineroder in allen Religionen; Vorrang der Loyalität gegenüber einem Freundvor der Wahrheit; Erwartbarkeit eines falschen Zeugnisses zugunsteneines Freundes; Glaube an Glücksbringer, Wahrsager, Wunderheiler undHoroskope; Glaubensbekehrung nach Schlüsselerlebnis; Gottesvorstellung;Beurteilung von Welt und Menschen als gut oder schlecht;gesellschaftliche Regeln oder Gottes Gesetze als Entscheidungsbasis fürrichtig oder falsch. Demographie: Geschlecht; Alter, Familienstand; Zusammenleben mit einemPartner; Schulbildung; Art und zeitlicher Umfang der beruflichenBeschäftigung; Beruf (ISCO-Code); privater oder öffentlicherArbeitgeber; berufliche Selbständigkeit und Anzahl der Angestellten;Vorgesetztenfunktion und Kontrollspanne; Wochenarbeitszeit; Einkommen;Haushaltsgröße; Haushaltszusammensetzung; Mitarbeiterzahl;Gewerkschaftsmitgliedschaft; Parteineigung und Wahlverhalten;Selbsteinstufung auf einem Links-Rechts-Kontinuum;Religionszugehörigkeit; Kirchgangshäufigkeit; Selbsteinstufung dersozialen Schichtzugehörigkeit. Zusätzlich verkodet wurden: Region; ländliche oder urbaneGegend; Ortsgröße; ethnische Identifikation. Attitude to religious practices.Topics:assessment of personal happiness;assessment of the responsibility of the governmentregarding creation of jobs and equalization of incomes;attitude to pre-marital as well as extra-marital sexual intercourse;attitude to homosexuality and abortion;judgement on distributoion of roles in marriage and attitude to working women;attitude to living together with a partner before marriagealso without intent to marry;tax honesty and attitude to honesty of citizens towards the government;trust in other people as well as institutions such as parliament,businesses, industry, churches, judiciary and schools;attitude to influence of church leaders on voters and governments;attitude to benefit of modern science;greater trust in science than in religion;more conflicts instead of peace from religions;intolerance of very religious people;too much influence of religion in one's country;frequency of personal honorary activities in the last yearin political, charitable, religious or other organizations;judgement on the power of churches and religious organizations;doubt or firm belief in God;perceived nearness to God;belief in a life after death, heaven, hell and miracles;attitude to the Bible;God is concerned with every human;fatalism;the meaning of life and Christian interpretation of life;religious tie at a turning point in life;religious affiliation of father, mother and spouse/partner;frequency of church attendance of father and mother;personal direction of belief and frequency of church attendance when young;frequency of prayer and participation in religious activities;self-classification of personal religiousness;truth in one or in all religions;priority for loyality to a friend before truth;anticipation of false testimony for the benefit of a friend;belief in lucky charms, fortune tellers, miracle healers and horoscopes;conversion of faith after crucial experience;concept of God;judgement on the world and people as good or bad;social rules or God's laws as basis for deciding between right and wrong.Demography:sex;age, marital status;living together with a partner;school education;type and time extent of occupation activity;occupation (ISCO-Code);private or public employer;occupational self-employment and number of employees;supervisor function and span of control;time worked each week;income;household size;composition of household;number of co-workers;union membership;party inclination and election behavior;self-classification on a left-right continuum;religious affiliation;frequency of church attendance;self-classification of social class.Also encoded was:region;rural or urban area;city size;ethnic identification.

This annual survey, fielded August to October 1999, was conducted in over 50 countries to solicit public opinion on social and political issues. Respondents were asked to give their opinion on the environment. Questions included the overall state that the environment is in, if the government has done too much, too little, or just the right amount concerning the environment, and the biggest threat to the environment for future generations. They were also queried on whether they thought their countries elections were free and fair, and what words best describe their perception of the government. Questions concerning religion were also asked. These focused on whether there is only one true religion, many true religions, or no essential truth in any religion, how important God is in their life, and praying and meditation. Respondents were asked to give their opinion on women's rights. Questions included whether they thought women have equal rights in their country, whether they thought education is more important for boys or girls, whether women need to have children in order to feel fulfilled, and whether women in advanced countries must insist more for the rights of women in the developing world. They were also asked to give their opinion on the issue of crime. They were asked how concerned they were about the level of crime in their country, if crime had increased or decreased in the last five years, how well the government was handling crime, and if they were for or against the death penalty. They were also asked what they thought matters most in life, and what they thought about the United Nations. Questions pertaining to human rights were also asked, such as whether discrimination based on sex, color, language, religion, or political opinion was taking place in their country. They were also asked if they thought that the use of torture was being documented, how effective stricter international laws would be in reducing torture, how effective more prosecutions of those suspected of torture would be in eliminating it, how effective greater public awareness of the incidence of torture would be in helping eliminate it, and how effective a grassroots campaign to eliminate torture would be. Respondents were also queried on the year 2000 computer problem. Demographics include sex, age, education, occupation, marital status, children under 15 living in household, religious denomination, religiosity, and region.

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.

synthetic-gold

Introduction

Record linkage is a complex process and is utilized to some extent in nearly every organization that works with modern human data records. People create methods for linking records on a case-by-case basis. Some may use basic matching between record 1 and record 2 as seen below ```python

Pseudo-Code!

if (r1.FirstName == r2.FirstName) & (r1.LastName == r2.LastName): out.match = True else: out.match = False ``` while others may choose to create more complex decision trees or even machine learning approaches to record linkage.

When people approach record linkage via machine learning (ML), they can match on a variety of fields, typically dependent on the forms used to collect data. While these ML-utilized fields can vary on a organization-to-organization level, there are several fields that appear more frequently than others. They are as follows: * First Name * Middle Name * Last Name * Date of Birth * Sex at Birth * Race * SSN (maybe) * Address house * Address zip * Address city * Address county * Address state * Phone * Email * Date Data Submitted

By comparing two records on all of these fields, ML record linkage models use complex logic to make the "Yes" or "No" decision on whether 2 records reflect the same individual. Record linkage can become difficult when individuals change addresses, adopt new last name, erroniously fill out data, or have information that closely resembles another individual (ex: twins).

The Problem

As described above, record linkage can have many complex elements to it.
Consider a situation where you are manually reviewing 2 records. These two records only contain basic information on the individuals and you are tasked to decide if Record #1 and Record #2 belong to the same person.

At a glance, these records are significantly different and you should therefore mark them as different persons. For the purposes of record linkage manual review, you probably made the correct decision. After all, for record linkage, most models prefer False Negatives to False Positives.

When groups validate record linkage models, they often turn to manually-reviewed record comparisons as their "gold-standard". There are two seperate marks of judgement for record linkage that I would like us to consider 1. Creating a model that simulates a human's decision making processs 2. Creating a model that seeks a deeper record equality "Truth"

I believe many groups aim for and are content with accomplishing goal #1. That approach is inarguably useful. However, I believe that it can be harmful in biases that it introduces. For example, it is biased against people who adopt new last names upon marriages/civil unions (more often "Female" Sex at Birth). Models that bias against non-american names can also produce high validation marks, but are flawed nonetheless. Consider the 2 records displayed earlier. There is a real chance that Wanda adopted a new last name and moved in the 6 years between when the data was collected.

Without relavant documentation (birth, marriage, ... , housing records), we have no way of knowing whether or not "Wanda Smith" is the same person as "Wanda Turner". It follows that treating manual review as a "gold-standard" fails to completely support goal #2.

The Solution

We hope to create a simulated society that can be used as absoulte truth. The simulated society will be built to reflect the population of Washington State. This will have a relational-database type structure with tables containing all relevant supporting structures for record linkage such as: * birth records * partnership (marriage) records * moving records

We hope to create a society with representative and diverse names, representative demographic breakdowns, and representative geographic population densities. The structure of the database will allow for "Time Travel" queries that allow a user to capture all data from a specific year in time.

By creating a simulated society, we will have absolute truth in determining whether record1 = record2. This approach will give us an opportunity to assess record linkage models considering goal #2.

Following Work

After we wrap this work, we will work on proccesses/functions for simulating human error in record filling. Also, functions to help support the process of bias-recognition in using our dataset as a test set.

Contact

PJ Gibson - peter.gibson@doh.wa.gov