The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

Analysis of ‘2020 US General Election Turnout Rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imoore/2020-us-general-election-turnout-rates on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Intro

Voter turnout is the percentage of eligible voters who cast a ballot in an election. Eligibility varies by country, and the voting-eligible population should not be confused with the total adult population. Age and citizenship status are often among the criteria used to determine eligibility, but some countries further restrict eligibility based on sex, race, or religion.

Context

The historical trends in voter turnout in the United States presidential elections have been determined by the gradual expansion of voting rights from the initial restriction to white male property owners aged 21 or older in the early years of the country's independence, to all citizens aged 18 or older in the mid-20th century. Voter turnout in United States presidential elections has historically been higher than the turnout for midterm elections. https://upload.wikimedia.org/wikipedia/commons/a/a7/U.S._Vote_for_President_as_Population_Share.png" alt="f">

Content

Turnout rates by demographic breakdown from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). This table are corrected for vote overreporting bias. For uncorrected weights see the source link.

Original source: https://data.world/government/vep-turnout

--- Original source retains full ownership of the source dataset ---

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

In this paper, we revisit the effect of ballot access laws on voter confidence in the outcome of elections. We argue that voter confidence is conditioned by partisanship. Democrats and Republicans view election laws through a partisan lens, which is especially triggered when coalitions lose. We used The Integrity of Voting data set, along with other data sets, to test our hypotheses. The sample frame for the Integrity of Voting Survey was eligible persons who voted in the 2020 Presidential elections with accessible internet email addresses. Our sample consisted of two samples from two different vendors. Surveys were conducted with 17,526 voters drawing on two independent samples of registered voters who reported voting in the 2020 Presidential election. Email addresses for registered voters in each state were purchased from L2, a commercial vendor specializing in obtaining email addresses for registered voters. Interviews were solicited from one million voters in all 50 states, with 10,770 completed interviews for a response rate of .011%. A second sample of internet interviews were solicited and completed with 6,756 2020 voters using Dynata’s proprietary select-in survey of voters in selected states with smaller populations of registered voters. A minimum of roughly 100 2020 election voters were interviewed in each state. Our state samples were weighted using a raking technique on age, race, gender, education, and vote mode demographics from the U.S. Census Bureau’s 2020 Voting and Registration in the Election of November 2020 supplement to the Current Population survey (2021), as well as party identification totals from post-election exit polls conducted by the Associated Press (2020). Surveys were conducted between the first week in December, 2020 and the first week in February 2021.

Terms of Access: By downloading the data, you agree to use the data only for academic research, agree not to share the data with outside parties, and agree not to attempt to re-identify individuals in the data set. We require this in order to protect the privacy of individuals in the data set and to comply with agreements made with TargetSmart. Abstract: We present the results of a large, $8.9 million campaign-wide field experiment, conducted among 2 million moderate and low-information “persuadable” voters in five battleground states during the 2020 US Presidential election. Treatment group subjects were exposed to an eight-month-long advertising program delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence the program increased or decreased turnout on average. We find evidence of differential turnout effects by modeled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (SE: 0.2pp) and decreased voting among Trump leaners by 0.3 percentage points (SE: 0.3pp), for a difference-in-CATES of 0.7 points that is just distinguishable from zero (t(1035571) = −2.09, p = 0.036, DIC = 0.7 points, 95% CI = [−0.014, −0.00]). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.

These data are derived from CANDIDATE NAME AND CONSTITUENCY TOTALS, 1788-1990 (ICPSR 0002). They consist of returns for two-thirds of all elections from 1788 to 1823 to the offices of president, governor, and United States representative, and over 90 percent of all elections to those offices since 1824. They also include information on United States Senate elections since 1912. Returns for one additional statewide office are included beginning with the 1968 election. This file provides a set of derived measures describing the vote totals for candidates and the pattern of contest in each constituency. These measures include the total number of votes cast for all candidates in the election, each candidate's percentage of the vote received, and several measures of the relative performance of each candidate. They are appended to the individual candidate records and permit extensive analysis of electoral contests over time. This dataset contains returns for all parties and candidates (as well as scattering vote) for general elections and special elections, including information on elections for which returns were available only at the constituency level. Included in this edition are data from the District of Columbia election for United States senator and United States representative. The offices of two senators and one representative were created by the "District of Columbia Statehood Constitutional Convention Initiative," which was approved by District voters in 1980. Elections for these offices were postponed until the 1990 general election. The three offices are currently local District positions, which will turn into federal offices if the District becomes a state.

The 2016 U.S. presidential election was contested by Donald J. Trump of the Republican Party, and Hillary Rodham Clinton of the Democratic Party. Clinton had been viewed by many as the most likely to succeed President Obama in the years leading up to the election, after losing the Democratic nomination to him in 2008, and entered the primaries as the firm favorite. Independent Senator Bernie Sanders soon emerged as Clinton's closest rival, and the popularity margins decreased going into the primaries. A few other candidates had put their name forward for the Democratic nomination, however all except Clinton and Sanders had dropped out by the New Hampshire primary. Following a hotly contested race, Clinton arrived at the Democratic National Convention with 54 percent of pledged delegates, while Sanders had 46 percent. Controversy emerged when it was revealed that Clinton received the support of 78 percent of Democratic superdelegates, while Sanders received just seven percent. With her victory, Hillary Clinton became the first female candidate nominated by a major party for the presidency. With seventeen potential presidential nominees, the Republican primary field was the largest in US history. Similarly to the Democratic race however, the number of candidates thinned out by the time of the New Hampshire primary, with Donald Trump and Ted Cruz as the frontrunners. As the primaries progressed, Trump pulled ahead while the remainder of the candidates withdrew from the race, and he was named as the Republican candidate in May 2016. Much of Trump's success has been attributed to the free media attention he received due to his outspoken and controversial behavior, with a 2018 study claiming that Trump received approximately two billion dollars worth of free coverage during the primaries alone. Campaign The 2016 presidential election was preceded by, arguably, the most internationally covered and scandal-driven campaign in U.S. history. Clinton campaigned on the improvement and expansion of President Obama's more popular policies, while Trump's campaign was based on his personality and charisma, and took a different direction than the traditional conservative, Republican approach. In the months before the election, Trump came to represent a change in how the U.S. government worked, using catchy slogans such as "drain the swamp" to show how he would fix what many viewed to be a broken establishment; painting Clinton as the embodiment of this establishment, due to her experience as First Lady, Senator and Secretary of State. The candidates also had fraught relationships with the press, although the Trump campaign was seen to have benefitted more from this publicity than Clinton's. Controversies Trump's off the cuff and controversial remarks gained him many followers throughout the campaign, however, just one month before the election, a 2005 video emerged of Trump making derogatory comments about grabbing women "by the pussy". The media and public's reaction caused many high-profile Republicans to condemn the comments (for which he apologized), with many calling for his withdrawal from the race. This controversy was soon overshadowed when it emerged that the FBI was investigating Hillary Clinton for using a private email server while handling classified information, furthering Trump's narrative that the Washington establishment was corrupt. Two days before the election, the FBI concluded that Clinton had not done anything wrong; however the investigation had already damaged the public's perception of Clinton's trustworthiness, and deflected many undecided voters towards Trump. Results Against the majority of predictions, Donald Trump won the 2016 election, and became the 45th President of the United States. Clinton won almost three million more votes than her opponent, however Trump's strong performance in swing states gave him a 57 percent share of the electoral votes, while Clinton took just 42 percent. The unpopularity of both candidates also contributed to much voter abstention, and almost six percent of the popular vote went to third party candidates (despite their poor approval ratings). An unprecedented number of faithless electors also refused to give their electoral votes to the two main candidates, instead giving them to five non-candidates. In December, it emerged that the Russian government may have interfered in this election, and the 2019 Mueller Report concluded that Russian interference in the U.S. election contributed to Clinton's defeat and the victory of Donald Trump. In total, 26 Russian citizens and three Russian organizations were indicted, and the investigation led to the indictment and conviction of many top-level officials in the Trump campaign; however Trump and the Russian government both strenuously deny these claims, and Trump's attempts to frame the Ukrainian government for Russia's involvement contributed to his impeachment in 2019.

This dataset represents the results of the 4-digit match performed using the Social Security - Help America Vote Verification (HAVV) system.

This research explores the impact of health on voter turnout, with the goal of uncovering important variation in dynamics across rural communities. Drawing on the results of county and individual-level analyses, including novel survey data from an Appalachian community, this study finds that health matters less for rural voters. Models using county-level data indicate that poor health is significantly and negatively related to voter turnout across counties, even when controlling for educational attainment, poverty, diversity, and political competition. However, health loses its explanatory power in rural counties once a control for religiosity is introduced. Health is also a less important predictor in rural places where there is a high cost of voting, a finding counter to the notion that high costs would uniformly amplify the negative effects of health disparities. Models using individual-level data provide support for many of these findings, while also generating new insights into the complexity of rural political behavior. Overall, this study suggests that place has an important role in understanding the engagement of American voters.

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. In this work, we identify 2107 registered US voters that account for 80% of the fake news shared on Twitter during the 2020 US presidential election by an entire panel of 664,391 voters. We find that supersharers are important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers have a significant overrepresentation of women, older adults, and registered Republicans. Supersharers' massive volume does not seem automated but is rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many., This dataset contains aggregated information necessary to replicate the results reported in our work on Supersharers of Fake News on Twitter while respecting and preserving the privacy expectations of individuals included in the analysis. No individual-level data is provided as part of this dataset. The data collection process that enabled the creation of this dataset leveraged a large-scale panel of registered U.S. voters matched to Twitter accounts. We examined the activity of 664,391 panel members who were active on Twitter during the months of the 2020 U.S. presidential election (August to November 2020, inclusive), and identified a subset of 2,107 supersharers, which are the most prolific sharers of fake news in the panel that together account for 80% of fake news content shared on the platform. We rely on a source-level definition of fake news, that uses the manually-labeled list of fake news sites by Grinberg et al. 2019 and an updated list based on NewsGuard ratings (commercial..., , # Supersharers of Fake News on Twitter

This repository contains data and code for replication of the results presented in the paper.

The folders are mostly organized by research questions as detailed below. Each folder contains the code and publicly available data necessary for the replication of results. Importantly, no individual-level data is provided as part of this repository. De-identified individual-level data can be attained for IRB-approved uses under the terms and conditions specified in the paper. Once access is granted, the restricted-access data is expected to be located under ./restricted_data.

The folders in this repository are the following:

Preprocessing

Code under the preprocessing folder contains the following:

1. source classifier - the code used to train a classifier based on NewsGuard domain flags to match the fake news labels source definition use in Grinberg et el. 2019 labels.
2. political classifier - the code used to identify political tweets, i...

Scholarship on women voters in the United States has focused on the gender gap showing that women are more likely to vote for Democratic Party candidates than men since the 1980s. The persistence of the gender gap has nurtured the conclusion that women are Democrats. This article presents evidence upending that conventional wisdom. Data from the American National Election Study are analyzed to demonstrate that white women are the only group of female voters who support Republican Party candidates for president. They have done so by a majority in all but 2 of the last 18 elections. The relevance of race for partisan choice among women voters is estimated with data collected in 2008, 2012, and 2016, and the significance of being white is identified after accounting for political party identification and other predictors.

This dataset contains voter registration data in Iowa by month and county starting with January 2000. It identifies the number of voters registered as Democrats, Republicans, other party or no party. Libertarians were reported separately March 2017 through January 2019, and beginning again in January 2023. The dataset also identifies the number of active and inactive voter registrations. Inactive voters are those to whom official mailings have been sent from the county auditor’s office, the notice was returned as undeliverable by the United States Postal Service and the voter has not responded to a follow up confirmation notice. [§48A.37]

This data is a recent survey data we collected by using Survey Monkey.

We asked how much people will vote Pete Buttigieg as President of the US, if he is nominee, and asked many reasons by scalar-bar questions which is created by us based on the initial open question survey.

This survey is completely original, not related with his campaign.

Insight Survey of Pete Buttigieg https://www.surveymonkey.com/r/L3H3CKD

We are looking for a data scientist or a causal analyst who has great ability to extract the insights from this type of data format. The winner of the best result will be honored by a spinning out company who will focus on commercial delivery of this analysis. Marketing Research has been struggling this type of open and close questions why people like a brand and products.

Find WHYs.

This repo contains the data introduced in

Immer, A.*, Kristof, V.*, Grossglauser, M., Thiran, P., Sub-Matrix Factorization for Real-Time Vote Prediction, KDD 2020

These data have been collected from OpenData.Swiss every two minutes on two different referendum vote days: May 19, 2019, and February 9, 2020. We use these data to make real-time predictions of the referenda outcome on www.predikon.ch. We publish here the raw data, as retrieved in JSON format from the API. We also provide a python script to help scraping the JSON files.

After unzipping the datasets, you can scrape the data by referendum vote day by doing:

from scraper import scrape_referenda

# Scrape the data from February 2, 2020.
data_dir = 'path/to/2020-02-09'
data = scrape_referenda(data_dir)

The data variable will be a list of datum dictionaries of the following structure:

{
 "vote": 6290,
 "municipality": 1,
 "timestamp": "2020-02-09T15:23:10",
 "num_yes": 222,
 "num_no": 482,
 "num_valid": 704,
 "num_total": 709,
 "num_eligible": 1407,
 "yes_percent": 0.3153409090909091,
 "turnout": 0.503909026297086
}

The datum is as follows:

• vote: vote ID as defined by OpenData.Swiss
• municipality: municipality ID as defined by OpenData.Swiss
• timestamp: date and time at which the JSON files has been published on OpenData.Swiss
• num_yes: number of "yes" in the municipality
• num_no: number of "no" in the municipality
• num_valid: number of valid ballots (the ones counting for the results)
• numb_total: total number of ballots (including invalid ones)
• num_eligible: number of registered voters
• yes_percent: percentage of "yes" (computed as `num_yes / num_valid`)
• turnout: turnout to the vote (computed as `num_total / num_eligible`)

Don't hesitate to reach out to us if you have any questions!

To cite this dataset:

@inproceedings{immer2020submatrix,
 author = {Immer, Alexander and Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick},
 title = {Sub-Matrix Factorization for Real-Time Vote Prediction},
 year = {2020},
 booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
}

The module was administered as a post-election interview. The resulting data are provided along with voting, demographic, district and macro variables in a single dataset.

CSES Variable List The list of variables is being provided on the CSES Website to help in understanding what content is available from CSES, and to compare the content available in each module.

Themes: MICRO-LEVEL DATA:

Identification and study administration variables: mode of interview; gender of interviewer; date questionnaire administered; election type; weighting factors; if multiple rounds: percent of vote selected parties received in first round; selection of head of state; direct election of head of state and process of direct election; threshold for first-round victory; selection of candidates for the final round; simple majority or absolute majority for 2nd round victory; primary electoral district of respondent; number of days the interview was conducted after the election

Demography: age; gender; education; marital status; union membership; union membership of others in household; business association membership, farmers´ association membership; professional association membership; current employment status; main occupation; socio economic status; employment type - public or private; industrial sector; current employment status, occupation, socio economic status, employment type - public or private and industrial sector of spouse; household income; number of persons in household; number of children in household under the age of 18; attendance at religious services; race; ethnicity; religiosity; religious denomination; language usually spoken at home; region of residence; rural or urban residence

Survey variables: political participation during the recent election campaign (persuade others, campaign activities) and frequency of political participation; contacted by candidate or party during the campaign; respondent cast a ballot at the current and the previous election; vote choice (presidential, lower house and upper house elections) at the current and the previous election; respondent cast candidate preference vote at the current election; most important issue; evaluation of governments performance concerning the most important issue and in general; satisfaction with the democratic process in the country; attitude towards selected statements: it makes a difference who is in power and who people vote for; democracy is better than any other form of government; respondent cast candidate preference vote at the previous election; judgement of the performance of the party the respondent voted for in the previous election; judgement how well voters´ views are represented in elections; party and leader that represent respondent´s view best; form of questionnaire (long or short); party identification; intensity of party identification; sympathy scale for selected parties; assessment of parties and political leaders on a left-right-scale; political participation during the last 5 years: contacted a politician or government, protest or demonstration, work with others who share the same concern; respect for individual freedom and human rights; assessment how much corruption is widespread in the country; self-placement on a left-right-scale; political information items

DISTRICT-LEVEL DATA:

number of seats contested in electoral district, number of candidates, number of party lists, percent vote of different parties, official voter turnout in electoral district

MACRO-LEVEL DATA:

percent of popular vote received by parties in current (lower house/upper house) legislative election; percent of seats in lower house received by parties in current lower house/upper house election; percentage of official voter turnout; number of portfolios held by each party in cabinet, prior to and after the most recent election; year of party foundation; ideological family the parties are closest to; European parliament political group and international organization the parties belong to; significant parties not represented before and after the election; left-right position of parties; general concensus on these left-right placements among informed observers in the country; alternative dimension placements; consensus on the alternative dimension placements; most salient factors in the election; consensus on the salience ranking; electoral alliances permitted during the election campaign; name of alliance and participant parties; number of elected legislative chambers; for lower house and upper house was asked: number of electoral segments; number of primary districts; number of seats; district magnitude (number of members elected from each district); number of secondary and tertiary electoral districts; compulsory voting; votes cast; voting procedure; transferrable votes; cumulated votes if more than one can be cast; party threshold; used electoral formula; party lists close, open, or flexible; parties can run joint lists; possibility of...

A polling place or polling location where voters cast their ballots on election day. One polling place may be associated with many voting precincts.

Boundaries of Orleans Parish voting precincts as defined by the New Orleans City Charter. New Orleans voting precincts are drawn according to the New Orleans Home Rule Charter as required by the State of Louisiana. A precinct is defined in the state of Louisiana's election code as the smallest political unit of a ward having defined geographical boundaries. Precinct boundaries were updated September 25, 2015, in order to satisfy population changes discovered by the Orleans Registrar of Voters Office. The changes have been made by the City of New Orleans and verified by the Louisiana Secretary of State's Office. Information about voter registation can be found here: https://www.sos.la.gov/ElectionsAndVoting/Pages/RegistrationStatisticsParish.aspx https://www.municode.com/library/la/new_orleans/codes/code_of_ordinances?nodeId=PTIICO_CH58EL_ARTIIELPRState LawRS 18:532. Establishment of precinctsA. Subject to the provisions of R.S. 18:532.1 and 1903, the governing authority of each parish shall establish precincts, define the territorial limits for which each precinct is established, prescribe their boundaries, and designate the precincts. The governing authority of each parish shall by ordinance adopt the establishment and boundaries of each precinct in accordance with the timetable as set forth herein and in accordance with R.S. 18:532.1.B.(1)(a) Each precinct shall be a contiguous, compact area having clearly defined and clearly observable boundaries coinciding with visible features readily distinguishable on the ground and approved extensions of such features, such as designated highways, roads, streets, rivers, or canals, and depicted on United States Bureau of the Census base maps for the next federal decennial census, except where the precinct boundary is coterminous with the boundary of a parish or an incorporated place when the boundaries of a single precinct contain the entire geographic area of the incorporated place. Except as otherwise provided in this Paragraph, on and after July 1, 1997, any precinct boundary which does not coincide with a visible feature shall be changed by the parish governing authority to coincide with a visible feature in accordance with R.S. 18:532.1.(b) For the purposes of this Paragraph, the term "approved extension" shall mean an extension of one visible feature to another visible feature which has been approved by the secretary of the Senate and the clerk of the House of Representatives or their designees and which is or which will be a census tabulation boundary.(2) No precinct shall be wholly contained within the territorial boundaries of another precinct, except that a precinct which contains the entire geographical area of an incorporated place and in which the total number of registered voters at the last general election was less than three hundred may be so contained.(3) No precinct shall contain more than two thousand two hundred registered voters within its geographic boundaries. Within thirty days after the completion of each canvass, the registrar of voters of each parish shall notify the parish governing authority of every precinct in the parish which contains more than two thousand two hundred registered voters within its geographic boundaries. Within sixty days of such notification, the parish governing authority shall divide such precincts by a visible feature in accordance with R.S. 18:532.1.(4)(a) No precinct shall contain less than three hundred registered voters within its geographical boundaries, except:(i) When necessary to make it more convenient for voters in a geographically isolated and unincorporated area to vote. A voter in a geographically isolated and unincorporated area shall mean a voter whose residen

Many explanations of the 2016 election result, a seemingly anomalous macro-level phenomenon, have centered on two seemingly anomalous micro-level phenomena: that many counties and citizens voted for Obama in 2008 and 2012, but then flipped and voted for Trump; and that low-education whites gave more of their votes to Trump than to Clinton. In this article, I first assess the novelty of these facts by placing them in the context of past elections. Compared to past presidential elections, the number of flips in 2016 was not unusually large, even in the Midwestern states. In contrast, the partisan divide by education was the highest ever in 2016. Using a series of counterfactual analyses, I then assess whether these factors were pivotal. If flipping counties had not flipped, Clinton would have won the electoral college by three votes, and if the lowest-educated 20% of counties voted as they did in 2012, Clinton would have won the electoral college by about thirty votes.

New democracies go to great lengths to implement institutional protections of the electoral process. However, in this paper we present evidence that shows that even in the United States—where the secret ballot has been in place for generations—doubts about the secrecy of the voting process are surprisingly prevalent. Many say that their cast ballot can be matched to their name or that others could observe their vote choices while they were voting. We find that people who have not previously voted are particularly likely to harbor doubts about the secrecy of voters’ ballots. Those who vote by mail in the privacy of their own homes also feel that others are able to discover their vote choices. Taken together, these findings suggest an important divergence between public perceptions about and the institutional status of the secret ballot in the United States, a divergence that may affect patterns of voting behavior and political participation.