As of February 2022, around **** percent of surveyed Iranians within the Islamic Republic of Iran considered voting for constitutionalists (pro-monarchists) parties if free elections were possible in their country. Only *** percent of respondents would vote for Marxists parties if there were free elections in Iran.

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website here. Project Summary Scholarship on human rights diplomacy (HRD)—efforts by government officials to engage publicly and privately with their foreign counterparts—often focuses on actions taken to “name and shame” target countries, because private diplomatic activities are unobservable. To understand how HRD works in practice, we explore a campaign coordinated by the US government to free twenty female political prisoners. We compare release rates of the featured women to two comparable groups: a longer list of women considered by the State Department for the campaign; and other women imprisoned simultaneously in countries targeted by the campaign. Both approaches suggest that the campaign was highly effective. We consider two possible mechanisms through which expressive public HRD works: by imposing reputational costs and by mobilizing foreign actors. However, in-depth interviews with US officials and an analysis of media coverage find little evidence of these mechanisms. Instead, we argue that public pressure resolved deadlock within the foreign policy bureaucracy, enabling private diplomacy and specific inducements to secure the release of political prisoners. Entrepreneurial bureaucrats leveraged the spotlight on human rights abuses to overcome competing equities that prevent government-led coercive diplomacy on these issues. Our research highlights the importance of understanding the intersection of public and private diplomacy before drawing inferences about the effectiveness of HRD. Data Generation We generated four sources of data for this project: 1. A dataset of political prisoners from 13 countries based on Amnesty International Urgent Action reports between 2000 and 2015. 2. Arrest and release information for a dataset of female political prisoners 3. A dataset on media attention based on both news articles from LexisNexis and online search trends from Google Trends 4. Interviews conducted with U.S. government officials and other human rights advocates involved in the #Freethe20 campaign to free political prisoners launched in September 2015 We used two sources of data for each of our two research questions. Our first research question was: Did the #Freethe20 campaign have an impact on the release rate of political prisoners? In an ideal world, we would have a comprehensive set of female political prisoners to compare with #Freethe20 prisoners. However, as we explain in the manuscript, in countries with more dire human rights situations, arrests often go unreported. In some cases, the sheer volume of political prisoners makes chronicling information about them challenging, if not impossible. Therefore, in order to construct a comparable set of cases, one strategy we used was to collect information from Amnesty International’s “Urgent Action” campaigns. To our knowledge, Amnesty International has the most comprehensive, publicly available list of contemporary political prisoners globally. Their records are public and searchable, which allowed us to construct a population of political prisoners from the countries targeted by the #Freethe20 campaign. We began our data collection with a base set of Urgent Actions metadata generated by Judith Kelley and Dan Nielson via webscraping from the Amnesty International website. Using a list of URLs that linked to each Urgent Action Report, we coded the name and sex of individuals featured in each Urgent Action Report from 2000 through September 2015 (the start of the #Freethe20 campaign) in the 13 countries featured in the campaign (Azerbaijan, Burma, China, Egypt, Ethiopia, Eritrea, Iran, North Korea, Russia, Syria, Uzbekistan, Venezuela, and Vietnam). Instructions about how we coded this information and sample documents are available in the QDR repository (QDR: MyrickWeinstein_codebook_urgentaction.pdf). After compiling a base dataset of individuals featured in Urgent Action reports, we identified the women in the dataset (~17% of entries) and conducted additional research about (1) whether these women could be classified as political prisoners, and (2) whether and when these women were released from prison, detention, or house arrest. Here, we relied on both follow-up reporting from Amnesty International as well as a variety of online news sources. We deposited the coding instructions for this process (MyrickWeinstein_codebook_releaseinfo.pdf) and also include documentation on additional online news sources that we used to make a judgment on a particular case. Our second question was: How and under what conditions did #Freethe20 affect the release rate of female political prisoners? To answer this question, we look at strategies of both public pressure and private, coercive diplomacy. For the former, we collected data on media attention and online search trends. We searched for newspapers and news articles that featured...

This dataset is about politicians. It has 5 rows and is filtered where the political party is Free Mps (Serbia). It features 10 columns including birth date, death date, country, and gender.

This Gallup Poll attempts to measure the opinions of Canadians on such topics as politics, freedom of speech, and education. The survey also contains questions intended to try and measure Canadians' knowledge on different topics concerning their country. Respondents were also asked questions so that they could be grouped \ according to geographic, political and social variables. Topics of interest include: Canada; car ownership; corporal punishiment; education of respondents; elections; freedom; free speech; money; phone ownership; political parties; politics; price levels; social security; taxation; travel; union membership; and working conditions in Canada. Basic demograhics variables are also included.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

Electoral Data for Ecuador

This dataset, from Crowdflower's Data For Everyone Library, provides text of 5000 messages from politicians' social media accounts, along with human judgments about the purpose, partisanship, and audience of the messages.

How was it collected?

Contributors looked at thousands of social media messages from US Senators and other American politicians to classify their content. Messages were broken down into audience (national or the tweeter’s constituency), bias (neutral/bipartisan, or biased/partisan), and finally tagged as the actual substance of the message itself (options ranged from informational, announcement of a media appearance, an attack on another candidate, etc.)

Acknowledgments

Data was provided by the Data For Everyone Library on Crowdflower.

Our Data for Everyone library is a collection of our favorite open data jobs that have come through our platform. They're available free of charge for the community, forever.

Inspiration

Here are a couple of questions you can explore with this dataset:

• what words predict partisan v. neutral messages?
• what words predict support messages v. attack messages?
• do politicians use Twitter and Facebook for different purposes? (e.g., Twitter for attack messages, Facebook for policy messages)?

The Data

The dataset contains one file, with the following fields:

• _unit_id: a unique id for the message
• _golden: always FALSE; (presumably whether the message was in Crowdflower's gold standard)
• _unit_state: always "finalized"
• _trusted_judgments: the number of trusted human judgments that were entered for this message; an integer between 1 and 3
• _last_judgment_at: when the final judgment was collected
• audience: one of national or constituency
• audience:confidence: a measure of confidence in the audience judgment; a float between 0.5 and 1
• bias: one of neutral or partisan
• bias:confidence: a measure of confidence in the bias judgment; a float between 0.5 and 1
• message: the aim of the message. one of: -- attack: the message attacks another politician
  -- constituency: the message discusses the politician's constituency
  -- information: an informational message about news in government or the wider U.S.
  -- media: a message about interaction with the media
  -- mobilization: a message intended to mobilize supporters
  -- other: a catch-all category for messages that don't fit into the other
  -- personal: a personal message, usually expressing sympathy, support or condolences, or other personal opinions
  -- policy: a message about political policy
  -- support: a message of political support
• message:confidence: a measure of confidence in the message judgment; a float between 0.5 and 1
• orig_golden: always empty; presumably whether some portion of the message was in the gold standard
• audience_gold: always empty; presumably whether the audience response was in the gold standard
• bias_gold: always empty; presumably whether the bias response was in the gold standard
• bioid: a unique id for the politician
• embed: HTML code to embed this message
• id: unique id for the message WITHIN whichever social media site it was pulled from
• label: a string of the form "From: firstname lastname (position from state)"
• message_gold: always blank; presumably whether the message response was in the gold standard
• source: where the message was posted; one of "facebook" or "twitter"
• text: the text of the message

Scholarship on human rights diplomacy (HRD)—efforts by government officials to engage publicly and privately with their foreign counterparts—often focuses on actions taken to “name and shame” target countries because private diplomatic activities are unobservable. To understand how HRD works in practice, we explore a campaign coordinated by the US government to free twenty female political prisoners called #Freethe20. We compare release rates of the featured women to two comparable groups: (1) a longer list of women considered by the State Department for the campaign, and (2) women imprisoned simultaneously in countries targeted by the campaign. Both approaches suggest that the campaign was highly effective. We consider two mechanisms through which expressive public HRD works: by imposing reputational costs and by mobilizing foreign actors. However, in-depth interviews with US officials and an analysis of #Freethe20 media coverage find little evidence of these mechanisms. Instead, we argue that public pressure resolved deadlock within the foreign policy bureaucracy, enabling private diplomacy and the use of specific inducements to secure the release of political prisoners. Entrepreneurial bureaucrats leveraged the spotlight on human rights abuses to overcome competing equities that prevent government-led coercive diplomacy on these issues. Our research highlights the importance of understanding the intersection of public and private diplomacy before drawing inferences about the effectiveness of HRD.

According to the source, as of 2024 Iran had a Freedom House score of *** point for the subcategory "Was the current head of government or other chief national authority elected through free and fair elections?" of the electoral process. Iran scored ***** points on overall political rights in the freedom survey, classifying it as not free on the international Freedom House ranking. The average global score for political freedom of countries was between ***** and **** which classifies them as partially free.

Political situation in Germany. Attitudes towards political parties.

Topics: Turnout intention and voting intention (Sunday question); Alternative voting intention; other electable party: Pirate Party; other electable party: Free voters; voting behaviour in the last federal election in 2009 (recall); positive or negative association with terms (people´s party, compassion, conservative, christian, social, close to the economy, middle-class, liberal, opportunities, achievement, cohesion, freedom, security, stability, order, performance justice, social market economy, centre, qualified immigration, budget consolidation, freedom of choice for families, intelligent saving, respect, demographic change, values, tradition, home, trust); annoyance about political decisions; issues about which one was annoyed; affected by political decisions (current); decisions by which one was personally affected (current); positive or negative impact of the decision; affected by political decisions (prospective); decisions by which one will be personally affected (prospective); party with which one feels most comfortable; subjective affiliation with ´little people´.

Political positions (politics takes care of the problems of the little people, concern about limiting living standards, debt reduction to maintain prosperity, public debt is good if it is made for the future of the children, fear of going out alone in the evening, problems keeping up with the pace of everyday life, state support for those who are willing to perform, acceptance of the performance principle, people´s parties prevent the assertion of individual interests, 30 km/h speed limit in cities, support for large-scale projects); association of certain terms with parties (people´s party, modern, compassionate, conservative, christian, down-to-earth, social, close to the economy, middle-class, liberal, advancement, opportunities, achievement, cohesion, freedom, security, stability, order, performance fairness, future, social market economy, centre, prosperity, qualified immigration, budget consolidation, freedom of choice for families, intelligent savings, demographic change, values, tradition, home, good governance, expertise, cares for citizens, party for all, can move Germany forward, strong leadership, energetic, honest, reliable, credible, responsible, trust).

Demography: age; highest school-leaving qualification; intended school-leaving qualification, completed studies; completed apprenticeship; occupation; profession; household size; frequency of churchgoing; party identification (direction, strength, stability); sex.

Additionally coded were: Federal state; inhabitant of place of residence; target persons in the household; number of telephone numbers; indicator replenishment sample; weighting factors.

We examine how Wikipedia data can be useful for political analysis. Our primary aim is to determine whether Wikipedia page views can be used as a strong proxy for name recognition. Name recognition is an important variable for political science research, but conducting surveys to discover the name recognition of politicians can be cost prohibitive for many researchers. To help researchers overcome this obstacle, we conducted a survey of Czech citizens asking them if they knew each of the 200 members of the Chamber of Deputies of the Czech Republic. Then, we downloaded Wikipedia page information on these deputies. We found that name recognition is strongly correlated with page views on Wikipedia. The more well-known deputies have significantly higher page views on Wikipedia. We recommend that political science researchers requiring an appropriate proxy for name recognition use the free Wikimedia API, which offers page information data.

Abstract copyright UK Data Service and data collection copyright owner.

The main aims of this research project were:
to generate a complete set of data of all incidents of intra-party dissension in the division lobbies of the House of Commons in the parliaments of 1979 to 1992 inclusively;
to identify and research the reasons for dissension in the Common's division lobbies and the events leading up to that dissension;
to analyse the data in order to identify trends in parliamentary behaviour and to generate and test explanations of that behaviour;
to identify and assess the consequences of changes in parliamentary behaviour for Parliament, for public policy, for the party in government, and for the legitimacy of the political system.
The data on free votes was a useful by-product of the above aims and objectives.
Two other datasets resulting from the same research project, containing dissension votes in the parliaments between 1979 and 1992, and the parliament of 1992-1997, are held at the Archive under SNs 3929 and 4055.
Main Topics:

This dataset records the occasions on which MPs from the two main British political parties (Conservatives and Labour) cast 'free' votes between 1979 and 1997. Each dataset covers one of the four parliaments of 1979, 1983, 1987 and 1992.
The dataset also contains additional data about the socio-economic and political backgrounds of each MP, covering their age, former occupation, education, political experience and gender, as well as their electoral situation.

Context

Political emails are rich in textual information. Despite the fact that political candidates and organizations regularly bombard their donors and base supporters with emails, they are relatively unstudied compared with other political textual data sources: tweets, press releases, speeches, news articles, etc.

I recently discovered that researchers at Princeton have also been tracking political emails. As of December 25th, 2020, their corpus [https://electionemails2020.org/](https://electionemails2020.org/ contains roughly 350,000 emails from 3100 senders between Dec. 2019 and present. However, their most recent paper only made use of around 100,000 emails, so there is likely a lot of duplication across their dataset. Thankfully, they have pledged to make their corpus publicly available soon. There is are a lot of potential insights to be found in this new data source.

As of December 25, 2020, my dataset contains roughly 80,000 unique emails from almost 1100 unique senders between 2015 and present. There is likely a lot of overlap with the Princeton dataset, however this dataset has some data from earlier years.

In a Google Big Query instance, I am continually adding to this dataset in real-time. I will periodically update this dataset with new data.

I'd love to add more emails to the dataset as well. When the Princeton dataset is released, I'll combine it here and work to eliminate duplicates. If you or someone you know have old emails political emails in your inbox and would like to contribute to this dataset, please reach out to me here or at noahfinberg@gmail.com. I have written a script that can scrape emails from gmail inboxes, parse them, and write them to a Google Big Query instance. We can walk through how to extract political emails from your inbox in a secure and private way together.

Content

This dataset contains 5 key columns: sender_name, email, subject , datetime, cleaned_content. They are pretty self explanatory. Cleaned content contains the text content for each email. I've already done some basic preprocessing, including: 1. eliminating newline and other unnecessary characters; 2. eliminating duplicates; 3. eliminating all external links from emails; and 4. eliminating personally identifying information to protect the source email addresses from which these emails were drawn.

Note: there is still likely a lot of duplication in my dataset after merging various sources of political emails together. Feel free to point out/remove duplicates. This dataset is in progress.

Acknowledgements

This project would not have been possible without the incredibly generous support of Derek Willis -- a data journalist @ ProPublica-- who contributed the majority of emails for this dataset. Derek has had the foresight to begin collecting these emails starting 5 years ago! In addition, Defending Democracy Together has an archive of political emails that I scraped in order to supplement this dataset: See https://politicalemails.org/.

Inspiration

I hope this dataset yields many insights about the evolution and nature of our political discourse.

I'll be using this data to better understand which people and topics campaigns perceive to be most mobilizing for their base -- as well as to better understand differences in candidates across the political spectrum. I have a background in researching political polarization and this data can be used to create a new common map for the ideological spectrum of different politicians and organizations.

Initial research questions: In light of the recent insurrection, it's clear that base political rhetoric has had profound and will continue to have profound implications for American democracy. This dataset provides a unique opportunity to understand which politicians and organizations mirrored Trump's rhetoric most closely. Who bears the most responsibility for flaming the fires of insurrection and conspiracy?

Which people and issues do campaigns perceive as most motivating for their base? For example, if Donald Trump tends to mention Ilhan Omar more than Alexandria Ocasio Cortez in campaign emails, that may signal the Trump campaign believes their base can be better motivated by islamophobia than by fears of socialism.

Which messages/issues do campaigns find most effective in mobilizing their base? How do different candidates frame these messages/issues? For example, for "taxes", "climate change", and "racial equality", how do various campaigns frame these issues?

Which campaigns are most inflammatory? Can we map politicians and political organizations to a common ideological spectrum? Are there interesting dimensions on which candidates differ in their language? **How have republicans changed their language in pre a...

This dataset is about politicians. It has 15 rows and is filtered where the political party is Free Motherland (Nagorno-Karabakh). It features 10 columns including birth date, death date, country, and gender.

The report examines the ways online Canadian adults are engaging politically on social media. This is the third and final report based on a census-balanced survey of 1,500 Canadians using quota sampling by age, gender, and geographical region. The other two reports in this series are: "The State of Social Media in Canada 2017" and "Social Media Privacy in Canada". The series is published by the Social Media Lab, an interdisciplinary research lab at Ted Rogers School of Management, Ryerson University. The lab studies how social media is changing the ways in which people communicate, share information, conduct business and how these changes are impacting our society.

Current state of the democratic consolidation in the newly implemented democracies. Topics: Political activities: discussions, convince friends, work in community, political meeting, contact politicians, work for party; meaning of democracy; democracy best form of government; democracy in (country) best; respect for individual human rights; importance of freedom and equality; Left-Right self placement; meaning of "Left" and "Right; membership in organisations and political parties; satisfaction with democracy; tolerance against: minority opinion, extremist demonstrations, free speech, too much freedom, critics on the way of life, right to own opinion, foreign critics; free market economy: right/wrong for country; satisfaction with free market; economic situation during present government, next year and compared to the socialist/communist regime; country´s economic situation during present government, next year and compared to the socialist/communist regime; conditions of workers, peasants, middle class, entrepreneurs during present government and compared to the socialist/communist regime; corruption during present government; public safety during present government, next year and compared to the socialist/communist regime; income differences right/wrong; one´s own financial situation compared to that of parents and neighbours; speed of change; satisfaction with present government and with the socialist/communist regime; communism good idea; better performance in: education, economy, poverty, black market, inflation, unemployment, public security, participation, corruption, public health, representation of interests, crime; pride in citizenship; pride to live in country; citizenship; women care of house; accept homosexuals; abortion; trust in institutions; medical care: self/governmental; income limits; government protects citizens; ecology vs. economy; conflicts: rich/poor, law-abiding/-breaker, speak language/not; (in Germany: conflicts between East/West), left/right, young/old, church moral/not, nationalists/others; police force against demonstrators; sentence against protestors, law against demonstrators, troops against strikes; big interests vs. all the people; trust in government; election best way to choose government; need for parliament; vote in last parliamentary election; vote intention; government responsible for providing job, health care, living standard for old and unemployed people, reduce income differences; governmental priorities; actions against bad governmental and local decisions; living conditions in western country; democracy in country: needs western development, never consolidated, same as in western countries, consolidation difficult process, notyet accomplished; Russia: country has own, difficult way; democracy problem will be solved; national political situation; change in political situation; state of democracy; parties: need of parties, no difference between parties, provide participation, for leaders´ interests; development since the end of the socialist/communist regime; characteristics of capitalist and socialist economy: strike, freedom, inequality, technical progress, wealth, selfishness, power, profit, justice, scarcity, humane, progress, planning, efficacy, repress, corruption; capitalist economy best; capitalist economy solves problems; management of industrial enterprises; close to party; political protest; one-person vs. multi-person system; interest in government; politicians against peoples´ interference; everybody can have say; better not get involved in politics; no trust in politicians; politicians seek views of people; people excl from power; make fortune get in politics; politicians only interested when trouble; participation is duty; satisfaction with changes: workers, engineers, artists, scientists, clerks, peasants, miners, entrepreneurs, politicians, army officers, policemen, leaders communist party; country where the living is better, better equality, people greater influence; importance in life: everybody voice in public matters, work for all, equality, everybody well-off, no arbitrary will, no big income differences, no state interference, live without worries, rest/entertain, free organisations, free speech, learn/access to culture; partner work outside; partner work full-/part-time; religion at birth; degree of religiosity; main language; in Russia: nationality; since when in neighbourhood; relations with neighbours; paid for work; reasons for not being paid;...

According to the 2024 Freedom House survey, Iran scored ***** on political rights, classifying it as not free on the international Freedom House ranking. The average global score for political freedom of countries was between ***** and **** which classifies them as partially free.

Democracy Index, produced by the Economist Intelligence Unit, provides a snapshot of the state of democracy in 165 independent states and two territories. It combines information on the extent to which citizens can choose their political leaders in free and fair elections, enjoy civil liberties, prefer democracy over other political systems, participate in politics, and have a functioning government that acts on their behalf. This collection includes only a subset of indicators from the source dataset.

This dataset contains information on how much money is spent by verified advertisers on political advertising across Google Ad Services. In addition, insights on demographic targeting used in political ad campaigns by these advertisers are also provided. Finally, links to the actual political ad in the Google Transparency Report are provided. Data for an election expires 7 years after the election. After this point, the data are removed from the dataset and are no longer available. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Replication Package Summary This replication package reproduces all empirical results in Free Market Initiatives: Benefits and Distortions from Political Power (Zhang and Zhao, Economic Inquiry, 2025). Please read the README. The replication workflow is implemented in Stata. All tables and figures in the manuscript can be replicated using the cleaned analysis dataset ftz_1020_reg.dta, which is already included in the package. Starting from this dataset, the full replication typically completes in approximately 4 hours on a standard desktop. Reconstructing the dataset entirely from raw firm-level sources (ssgs_raw.dta and gyqy_data_raw.dta) is optional and requires substantial storage (≈12GB) and time (up to 48–72 hours). These raw datasets are provided solely for replication purposes and may not be redistributed. All processed datasets required to replicate the manuscript’s results are included and may be used for academic research with appropriate citation. To replicate: Set the working directory in the .do files. Install required Stata packages (see README). Run run_all_1020.do to generate all results in the output/ folder. A small number of outputs require manual formatting and arranging, as noted in the README. This package allows full reproduction of all main tables, appendix tables, and figures reported in the paper.