Since 1988, the share of adults in the U.S. who identify as political independents has continued to grow, often surpassing the that of Democrats or Republicans. In 2024, approximately ** percent of adults rejected identification with the major parties, compared to ** percent of respondents identified with the Democratic Party, and ** percent with the Republican Party.

In the last few decades, the Democratic Party has often pulled ahead of the Republican Party in terms of party identification. However, 2022 saw a shift in party identification, with slightly more Americans identifying with the Republican Party for the first time since 2011, when both parties stood at ** percent in 2011. These values include not only those surveyed who identified with a major political party, but also those who identified as independent, but have leanings towards one party over another.

This dataset contains counts of voter registration and voter turnout for all counties in the United States for the years 2004-2022. It also contains measures of each county's Democratic and Republican partisanship, including six-year longitudinal partisan indices for 2006-2022.

This dataset contains simulated data for social media users' demographics, behaviors, and perceptions related to political content. It includes features such as age, gender, education level, occupation, social media usage frequency, exposure to political content, and perceptions of accuracy and relevance.

the features included in the "Social Media Political Content Analysis Dataset":

1. Age: Age of the user.
2. Gender: Gender identity of the user.
3. Education Level: Highest level of education attained by the user.
4. Occupation: Current occupation of the user.
5. Political Affiliation: Political leaning or affiliation of the user (e.g., Liberal, Conservative, Independent).
6. Geographic Location: Country or region where the user is located (e.g., USA, UK, Canada, Australia).
7. Social Media Usage Frequency: Frequency of social media usage by the user (e.g., 0-1 hour, 1-2 hours, 2-4 hours, 4+ hours).
8. Preferred Social Media: Social media platform preferred by the user (e.g., Facebook, Twitter, Instagram).
9. Political Content Exposure: Frequency of exposure to political content on social media (e.g., Once a day, Few times a week, Rarely, Several times a day).
10. Types of Political Content: Types of political content consumed by the user (e.g., News articles, Opinion pieces, Memes).
11. Sources of Political Content: Sources from which the user obtains political content (e.g., Mainstream media, Political parties, Independent bloggers).
12. Recency of Exposure: Recency of the user's exposure to political content (e.g., Within the last hour, Within the last 24 hours, Within the last week, Longer than a week ago).
13. Interactions Frequency: Frequency of user interactions with political content on social media (e.g., Once a day, Few times a week, Rarely, Several times a day).
14. Political Content Topics: Topics of political content that interest the user (e.g., Economy, Healthcare, Immigration, Environment).
15. Perception of Accuracy: User's perception of the accuracy of political content on social media (e.g., Very accurate, Somewhat accurate, Not accurate).
16. Awareness of Algorithms: Whether the user is aware of algorithms that determine their social media feed (e.g., Yes, No).
17. Perception of Relevance: User's perception of the relevance of political content on social media (e.g., Very relevant, Somewhat relevant, Not relevant).
18. Personal Impact: User's perception of the personal impact of political content on social media (e.g., Strong impact, Moderate impact, No impact).
19. Trust in Social Media: User's level of trust in social media as a source of political information (e.g., Trust a lot, Trust somewhat, Do not trust).
20. Concerns about Algorithms: User's level of concern about algorithms shaping their social media experience (e.g., Very concerned, Somewhat concerned, Not concerned).
21. Overall Quality of Discourse: User's perception of the overall quality of political discourse on social media (e.g., High quality, Moderate quality, Low quality).
22. Views on Influence: User's perception of the influence of political content on social media (e.g., Very influential, Somewhat influential, Not influential).
23. Suggestions for Improvement: User's suggestions for improving the quality or experience of political content on social media (e.g., Increase transparency, Provide more diverse sources, Improve fact-checking, Enhance user controls).

Audio-visual data is ubiquitous in politics. Campaign advertisements, political debates, and the news cycle all constantly generate sound bites and imagery, which in turn inform and affect voters. Though these sources of information have been a topic of research in political science for decades, their study has been limited by the cost of human coding. To name but one example, to answer questions about the effects of negative campaign advertisements, humans must watch tens of thousands of advertisements and manually label them. And even if the necessary resources can be mustered for such a study, future researchers may be interested in a different set of labels, and so must either recode every advertisement or discard the exercise entirely. Through three separate models, this dissertation resolves this limitation by developing automated methods to study the most common types of audio-video data in political science. The first two models are neural networks, the third a hierarchical hidden Markov model. In Chapter 1, I introduce neural networks and their complications to political science, building up from familiar statistical methods. I then develop a novel neural network for classifying newspaper articles, using both the text of the article and the imagery as data. The model is applied to an original data set of articles about fake news, which I collected by developing and deploying bots to concurrently crawl the online pages of newspapers and download news text and images. This is a novel engineering effort that future researchers can leverage to collect effectively limitless amounts of data about the news. Building on the methodological foundations established in Chapter 1, in Chapter 2 I develop a second neural network for classifying political video and demonstrate that the model can automate classification of campaign advertisements, using both the visual and the audio information. In Chapter 3 (joint with Dean Knox), I develop a hierarchical hidden Markov model for speech classification and demonstrate it with an application to speech on the Supreme Court. Finally, in Chapter 4 (joint with Volha Charnysh and Prerna Singh), I demonstrate the behavioral effects of imagery through a dictator game in which a visual image reduces out-group bias. In sum, this dissertation introduces a new type of data to political science, validates its substantive importance, and develops models for its study in the substantive context of politics.

This study contains selected demographic, social, economic, public policy, and political comparative data for Switzerland, Canada, France, and Mexico for the decades of 1900-1960. Each dataset presents comparable data at the province or district level for each decade in the period. Various derived measures, such as percentages, ratios, and indices, constitute the bulk of these datasets. Data for Switzerland contain information for all cantons for each decennial year from 1900 to 1960. Variables describe population characteristics, such as the age of men and women, county and commune of origin, ratio of foreigners to Swiss, percentage of the population from other countries such as Germany, Austria and Lichtenstein, Italy, and France, the percentage of the population that were Protestants, Catholics, and Jews, births, deaths, infant mortality rates, persons per household, population density, the percentage of urban and agricultural population, marital status, marriages, divorces, professions, factory workers, and primary, secondary, and university students. Economic variables provide information on the number of corporations, factory workers, economic status, cultivated land, taxation and tax revenues, canton revenues and expenditures, federal subsidies, bankruptcies, bank account deposits, and taxable assets. Additional variables provide political information, such as national referenda returns, party votes cast in National Council elections, and seats in the cantonal legislature held by political groups such as the Peasants, Socialists, Democrats, Catholics, Radicals, and others. Data for Canada provide information for all provinces for the decades 1900-1960 on population characteristics, such as national origin, the net internal migration per 1,000 of native population, population density per square mile, the percentage of owner-occupied dwellings, the percentage of urban population, the percentage of change in population from preceding censuses, the percentage of illiterate population aged 5 years and older, and the median years of schooling. Economic variables provide information on per capita personal income, total provincial revenue and expenditure per capita, the percentage of the labor force employed in manufacturing and in agriculture, the average number of employees per manufacturing establishment, assessed value of real property per capita, the average number of acres per farm, highway and rural road mileage, transportation and communication, the number of telephones per 100 population, and the number of motor vehicles registered per 1,000 population. Additional variables on elections and votes are supplied as well. Data for France provide information for all departements for all legislative elections since 1936, the two presidential elections of 1965 and 1969, and several referenda held in the period since 1958. Social and economic data are provided for the years 1946, 1954, and 1962, while various policy data are presented for the period 1959-1962. Variables provide information on population characteristics, such as the percentages of population by age group, foreign-born, bachelors aged 20 to 59, divorced men aged 25 and older, elementary school students in private schools, elementary school students per million population from 1966 to 1967, the number of persons in household in 1962, infant mortality rates per million births, and the number of priests per 10,000 population in 1946. Economic variables focus on the Gross National Product (GNP), the revenue per capita per household, personal income per capita, income tax, the percentage of active population in industry, construction and public works, transportation, hotels, public administration, and other jobs, the percentage of skilled and unskilled industrial workers, the number of doctors per 10,000 population, the number of agricultural cooperatives in 1946, the average hectares per farm, the percentage of farms cultivated by the owner, tenants, and sharecroppers, the number of workhorses, cows, and oxen per 100 hectares of farmland in 1946, and the percentages of automobiles per 1,000 population, radios per 100 homes, and cinema seats per 1,000 population. Data are also provided on the percentage of Communists (PCF), Socialists, Radical Socialists, Conservatives, Gaullists, Moderates, Poujadists, Independents, Turnouts, and other political groups and p

American politics scholarship has relied extensively on self-reported measures of ideology. We evaluate these widely used measures through an original national survey. Descriptively, we show that Americans’ understandings of “liberal” and “conservative” are weakly aligned with conventional definitions of these terms and that such understandings are heterogeneous across social groups, casting doubt on the construct validity and measurement equivalence of ideological self-placements. Experimentally, we randomly assign one of three measures of ideology to each respondent: (1) the standard ANES question, (2) a version that adds definitions of “liberal” and “conservative,” and (3) a version that keeps these definitions but removes ideological labels from the question. We find that the third measure, which helps to isolate symbolic ideology from operational ideology, shifts self-reported ideology in important ways: Democrats become more conservative, and Republicans more liberal. These findings offer first-cut experimental evidence on the limitations of self-reported ideology as a measure of operational ideology, and contribute to ongoing debates about the use of ideological self-placements in American politics.

A survey measuring levels of engagement with political news in the United States found that older adults were by far the most likely to get news about politics and elections from journalists and news organizations, with 78 percent of adults aged 65 years or above saying they did so. Meanwhile, adults aged 18 to 29 years old were the likeliest to go to friends, family, or neighbors for updates about elections and politics.

This data collection contains political, economic, and social data covering five years: 1962, 1966, 1971, 1976, and 1978. Information was collected from 57 counties in the state of New York, excluding those in New York City. The variables include taxes, revenues, expenditures, federal aid, demographic variables, and vote returns for president, senator, and governor. The data are arranged first by year, then by county, and then by deck number.

EPILOGUE: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2Fa5eefdb31428bd5ce99cdf76fa484a63%2Fmap.jpg?generation=1733007717460285&alt=media" alt="">

FINAL UPDATE: It's election night, and the results are coming in. The final update includes the latest poll data from 538, which is from two days ago. Thanks all for following the development of this dataset.

OCTOBER UPDATE: The past month has been typical of the final weeks before the election - rallies, interviews, and advertising. This update includes a transcript of the VP debate between Walz and Vance, and the latest poll summaries.

SEPTEMBER UPDATE: Trump and Harris had their first debate. This update includes the transcript and recent poll results. Also, there was a second attempt to kill former President Trump! No shots fired though on this one. You'll see aerial diagrams of both attempts in the dataset.

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LATE AUGUST UPDATE: The Democratic Party replaced President Biden with his VP, Kamala Harris. It's now Trump v Harris along with one nominee from each of the smaller factions.

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AUGUST UPDATE: This election season just gets crazier and crazier. You'll see new data related to the assassination attempt on former President Trump. There are transcripts of Secret Service hearings and an annotated image of the rally area.

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JULY UPDATE: Added the transcript of the debate between Trump and Biden.

MAY UPDATE: Added some new polls and also a meta-poll assessing the quality of select pollsters.

APRIL UPDATE : The dataset now contains approval ratings for sitting presidents, which includes Biden and Trump.

MARCH UPDATE: As of last week, the presumptive nominees are Joe Biden(D) and Donald Trump(R). They also ran against each other in 2020. Robert F Kennedy Jr is running as an independent.

Presidential elections occur quadrennially in years evenly divisible by 4, on the first Tuesday after November 1. Presidential candidates from the major political parties usually declare their intentions to run as early as the spring of the previous calendar year before the election. The two major parties each nominate one candidate through a process of primary elections and nominating conventions during the election year. (source: Wikipedia)

This dataset contains data on candidates, primary/caucus results, polls, and debate transcripts. Updates and additional data will be added as the landscape develops.

Note: Version 3 of this dataset contains previous coverage of the 2022 Congressional Mid-term Elections.

Abstract: The Database on Ideology, Money in Politics, and Elections (DIME) is intended as a general resource for the study of campaign finance and ideology in American politics. The database was developed as part of the project on Ideology in the Political Marketplace, which is an on-going effort to perform a comprehensive ideological mapping of political elites, interest groups, and donors using the common-space CFscore scaling methodology (Bonica 2014). Constructing the database required a large-scale effort to compile, clean, and process data on contribution records, candidate characteristics, and election outcomes from various sources. The resulting database contains over 130 million political contributions made by individuals and organizations to local, state, and federal elections spanning a period from 1979 to 2014. A corresponding database of candidates and committees provides additional information on state and federal elections. The DIME+ data repository on congressional activity extends DIME to cover detailed data on legislative voting, lawmaking, and political rhetoric. (See http://dx.doi.org/10.7910/DVN/BO7WOW for details.) The DIME data is available for download as a standalone SQLite database. The SQLite database is stored on disk and can be accessed using a SQLite client or queried directly from R using the RSQLite package. SQLite is particularly well-suited for tasks that require searching through the database for specific individuals or contribution records. (Click here to download.) Overview: The database is intended to make data on campaign finance and elections (1) more centralized and accessible, (2) easier to work with, and (3) more versatile in terms of the types of questions that can be addressed. A list of the main value-added features of the database is below: Data processing: Names, addresses, and occupation and employer titles have been cleaned and standardized. Unique identifiers: Entity resolution techniques were used to assign unique identifiers for all individual and institutional donors included in the database. The contributor IDs make it possible to track giving by individuals across election cycles and levels of government. Geocoding: Each record has been geocoded and placed into congressional districts. The geocoding scheme relies on the contributor IDs to assign a complete set of consistent geo-coordinates to donors that report their full address in some records but not in others. This is accomplished by combining information on self-reported address across records. The geocoding scheme further takes into account donors with multiple addresses. Geocoding was performed using the Data Science Toolkit maintained by Pete Warden and hosted at http://www.datasciencetoolkit.org/. Shape files for congressional districts are from Census.gov (http://www.census.gov/rdo/data). Ideological measures: The common-space CFscores allow for direct distance comparisons of the ideal points of a wide range of political actors from state and federal politics spanning a 35 year period. In total, the database includes ideal point estimates for 70,871 candidates and 12,271 political committees as recipients and 14.7 million individuals and 1.7 million organizations as donors. Corresponding data on candidates, committees, and elections: The recipient database includes information on voting records, fundraising statistics, election outcomes, gender, and other candidate characteristics. All candidates are assigned unique identifiers that make it possible to track candidates if they campaign for different offices. The recipient IDs can also be used to match against the database of contribution records. The database also includes entries for PACs, super PACs, party committees, leadership PACs, 527s, state ballot campaigns, and other committees that engage in fundraising activities. Identifying sets of important political actors: Contribution records have been matched onto other publicly available databases of important political actors. Examples include: Fortune 500 directors and CEOs: (Data) (Paper) Federal court judges: (Data) (Paper} State supreme court justices: (Data) (Paper} Executives appointees to federal agencies: (Data) (Paper) Medical professionals: (Data) (Paper)

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

Graph and download economic data for Equity Market Volatility Tracker: Elections And Political Governance (EMVELECTGOVRN) from Jan 1985 to Oct 2025 about political, volatility, uncertainty, equity, government, and USA.

This study investigated the cognitive processing of true and false political information. Specifically, it examined the impact of source credibility on the assessment of veracity when information comes from a polarizing source (Experiment 1), and effectiveness of explanations when they come from one's own political party or an opposition party (Experiment 2). These experiments were conducted prior to the 2016 Presidential election. Participants rated their belief in factual and incorrect statements that President Trump made on the campaign trail; facts were subsequently affirmed and misinformation retracted. Participants then re-rated their belief immediately or after a delay. Experiment 1 found that (i) if information was attributed to Trump, Republican supporters of Trump believed it more than if it was presented without attribution, whereas the opposite was true for Democrats and (ii) although Trump supporters reduced their belief in misinformation items following a correction, they did not change their voting preferences. Experiment 2 revealed that the explanation's source had relatively little impact, and belief updating was more influenced by perceived credibility of the individual initially purporting the information. These findings suggest that people use political figures as a heuristic to guide evaluation of what is true or false, yet do not necessarily insist on veracity as a prerequisite for supporting political candidates.

This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

This dataset contains expenditures made by Washington State Candidates and Political Committees for the last 10 years as reported to the PDC on forms C3, C4, Schedule C and their electronic filing equivalents. In-kind contributions are included in this data set as they are considered as both a contribution and expenditure. In-kind contributions are also included in the data set "Contributions to Candidates and Political Committees" For candidates, the number of years is determined by the year of the election, not necessarily the year the expenditure was reported. For political committees, the number of years is determined by the calendar year of the reporting period. Candidates and political committees choosing to file under "mini reporting" are not included in this dataset. See WAC 390-16-105 for information regarding eligibility. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

This dataset contains descriptive statistics for the eleven political talk shows.

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

According to a 2023 survey, Americans between 18 and 29 years of age were more likely to identify with the Democratic Party than any other surveyed age group. While 39 percent identified as Democrats, only 14 percent identified ad Republicans. However, those 50 and older identified more with the Republican Party.