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.

This dataset is crawled from three popular on-line social networks (OSNs), namely, Twitter, Facebook and Foursquare. We collected this dataset as follows. We first gathered a set of Singapore-based Twitter users who declared Singapore as location in their user profiles. From the Singapore-based Twitter users, we retrieve a subset of Twitter users who declared their Facebook or Foursquare accounts in their short bio description. In total, we collected 1,998 Twitter-Facebook user identity pairs (known as TW-FB ground truth matching pairs}, and 3,602 Twitter-Foursquare user identity pairs (known as TW-FQ ground truth matching pairs). To simulate a real-world setting, where a user identity in the source OSN may not have its corresponding matching user identity in the target OSN, we expanded the datasets by adding Twitter, Facebook and Foursquare users who are connected to users in the TW-FB ground truth matching pairs and TW-FQ ground truth matching pairs sets. Note that isolated users who do not have links to other users are removed from the data sets. After collecting the datasets, we extract the following user features using the OSNs' APIs. • Username: The username of the account. • Screen name: The natural name of the user account. It is usually formed using the first and last name of the user. • Profile Image: The thumbnail or image provided by the user to visually present herself. • Network: The relationship links between users.

We provide text metadata, image frames, and thumbnails of YouTube videos classified as harmful or harmless by domain experts, GPT-4-Turbo, and crowdworkers. Harmful videos are categorized into one or more of six harm categories: Information harms (IH), Hate and Harassment harms (HH), Clickbait harms (CB), Addictive harms (ADD), Sexual harms (SXL), and Physical harms (PH).

This repository includes the text metadata and a link to external cloud storage for the image data.

Text Metadata

Explanations about the indicators

Image data

Related works (in preprint)

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 ...

Over the past half-century, numerous transitional justice (TJ) measures have been implemented globally. While much research has examined different TJ modalities in the aftermath of authoritarian rule and armed conflict, a growing body of work recognizes TJ outside of political transitions. We study a noteworthy export from transitional to non-transitional settings: truth commissions. Building on scholarship on TJ in established democracies, we introduce new quantitative data from the Varieties of Truth Commissions Project on truth commissions in an overlooked but significant case: the United States. The data captures 20 past, present and proposed official US truth commissions, most of them at the subnational level. Though their mandates vary considerably, they all address racial injustice, with an emphasis on anti-Indigenous and anti-Black violence. We elaborate on trends in the data and discuss the implications for unfolding efforts to reckon with historical and contemporary racial violence and injustice in the United States.

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.

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.

The Residential School Locations Dataset [IRS_Locations.csv] 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 Indian Residential School 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.

Sentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document's stance with respect to a given target--how positively or negatively it frames a specific idea, individual, or group--as this reflects the author's underlying political attitudes. In this paper we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document's sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.

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

The Residential Schools Locations Dataset in shapefile format 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 Indian Residential School Settlement Agreement are included in this data set, 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 data set 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 data set,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. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

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.

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.

We propose a framework for meta-analysis of qualitative causal inferences. We integrate qualitative counterfactual inquiry with an approach from the quantitative causal inference literature called extreme value bounds. Qualitative counterfactual analysis uses the observed outcome and auxiliary information to infer what would have happened had the treatment been set to a different level. Imputing missing potential outcomes is hard and when it fails, we can fill them in under best- and worst-case scenarios. We apply our approach to 63 cases that could have experienced transitional truth commissions upon democratization, 8 of which did. Prior to any analysis, the extreme value bounds around the average treatment effect on authoritarian resumption are 100 percentage points wide; imputation shrinks the width of these bounds to 51 points. We further demonstrate our method by aggregating specialists' beliefs about causal effects gathered through an expert survey, shrinking the width of the bounds to 44 points.

We run an experimental study using sender-receiver games to evaluate how senders' willingness to lie to others compares to their willingness to tell hard truths, i.e., promote an outcome that the sender know is unfair to the receiver without explicitly lying. Unlike in previous work on lying when it has consequences, we do not find that antisocial behavior is less frequent when it involves lying than when it does not. In fact, we find the opposite result in the setting where there is social contact between senders and receivers, and receivers have enough information to judge whether they have been treated unfairly. In this setting, we find that senders prefer to hide behind a lie and implement the antisocial outcome by being dishonest rather than by telling the truth. These results are consistent with social image costs depending on the social proximity between senders and receivers, especially when receivers can judge the kindness of the senders' actions.

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 field of “transitional justice” refers to a range of processes and mechanisms for accountability, truth-seeking, and reconciliation that governments and communities pursue in the aftermath of major societal traumas, including civil war, mass atrocities, and authoritarianism. This relatively new field emerged in the 1980s as scholars, practitioners, and policymakers looked for guidance to support post-authoritarian and post-communist transitions to democracy in East- ern Europe and Latin America. Since then, the field has grown rapidly—so rapidly that it is out- pacing its capacity to learn from past mistakes. Recent methodological advances in the study of public attitudes about transitional justice through quantitative surveys and qualitative interview methods provide unprecedented insights into how different mechanisms—including domestic and international prosecutions, truth commissions, amnesty laws, and compensation—are perceived by their intended beneficiaries. The results have been troubling. Numerous studies in diverse con- texts found that some of the most well-known transitional justice mechanisms, including those employed in South Africa, Rwanda, and Cambodia, failed to achieve their objectives of peacebuild- ing and reconciliation. In some cases, these policies had harmful consequences for their intended beneficiaries, including retraumatization and perceived “justice gaps” between victims’ preferred remedies and their actual outcomes. There is an urgent need for the field of transitional justice to learn from this growing body of empirical research to develop evidence-based policies and programs that achieve their intended objectives. This Feature critically reviews the intellectual development of the field, consolidating empirical findings of relevant studies across disciplines—law, political science, sociology, econom- ics, public health, psychology, and anthropology—and identifying open debates and questions for future research. We focus on research about public attitudes toward transitional justice in the com- munities directly impacted by conflict. In addition to reviewing previous research, we present new data from original public opinion surveys in Iraq and Ukraine relevant to ongoing transitional justice efforts in those countries. We use this evidence to identify lessons learned, including mis- takes, in the design and implementation of previous transitional justice processes. We conclude by discussing the normative and prescriptive implications of our findings for efforts to improve future transitional justice laws and policies.

LiveJournal social network and ground-truth communities

https://snap.stanford.edu/data/com-LiveJournal.html

Dataset information

LiveJournal (http://www.livejournal.com/) 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 (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Dataset statistics
Nodes 3,997,962
Edges 34,681,189
Nodes in largest WCC 3997962 (1.000)
Edges in largest WCC 34681189 (1.000)
Nodes in largest SCC 3997962 (1.000)
Edges in largest SCC 34681189 (1.000)
Average clustering coefficient 0.2843
Number of triangles 177820130
Fraction of closed triangles 0.04559
Diameter (longest shortest path) 17
90-percentile effective diameter 6.5

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-lj.ungraph.txt.gz Undirected LiveJournal network
com-lj.all.cmty.txt.gz LiveJournal communities
com-lj.top5000.cmty.txt.gz LiveJournal communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 0-based, with nodes numbering 0 to
4,036,537.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
LiveJournal network, a matrix of size n-by-n with n=3,997,962, 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 (0-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 664,414
which represents the communities in the com-lj.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-lj.top5000.cmty.txt file.