The Cumulative Report includes complete official election results for the 2020 Presidential General Election as of November 29, 2020. Results are released in three separate reports: The Vote By Mail 1 report contains complete results for ballots received by the Board of Elections on or before October 21, 2020, that could be accepted and opened before Election Day. The Vote By Mail 2 Canvass report contains complete results for all remaining Vote By Mail ballots that were received in a drop box or in person at the Board of Elections by 8:00pm on November 3, or were postmarked by November 3 and received timely by the Board of Elections by 10:00am on Friday, November 13. The Vote By Mail 2 Canvass begins on Thursday, November 5. The Provisional Canvass contains complete results for all provisional ballots issued to voters at Early Voting or on Election Day. For more information on this process, please visit the 2020 Presidential General Election Ballot Canvass webpage at https://www.montgomerycountymd.gov/Elections/2020GeneralElection/general-ballot-canvass.html. For turnout information, please visit the Maryland State Board of Elections Press Room webpage at https://elections.maryland.gov/press_room/index.html.

This dataset contains county-level returns for presidential elections from 2000 to 2024.

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).

February 18, 2020 - Spring Primary

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Analysis of ‘🗳 VEP Turnout’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vep-turnoute on 13 February 2022.

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

About this dataset

Files:

National level

• U.S. VEP Turnout 1789-Present-Statistics - The complete time series of national presidential and midterm general election turnout rates from 1787-present.

National and state level

• 1980-2014 November General Election - Turnout Rates
• 2016 November General Election - Turnout Rates
• 2018 November General Election - Turnout Rates
• 2020 November General Election - Turnout Rates

Turnout rates by demographic breakdown, 1986-2018, from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). These tables are corrected for vote overreporting bias. For uncorrected weights see the source link.

• Turnout Rate 1986-2018 by Age
• Turnout Rate 1986-2018 by Education
• Turnout Rate 1986-2018 by Race and Ethnicity

For more information on these files see the source link below.

Source: Data prepared and maintained by Dr. Michael P. McDonald at the University of Florida, at electproject.org

Updated: synced from source weekly

License: CC-BY

This dataset was created by Government and contains around 100 samples along with Unnamed: 7, Denominators, technical information and other features such as: - Unnamed: 4 - Unnamed: 5 - and more.

How to use this dataset

• Analyze Unnamed: 16 in relation to Unnamed: 14
• Study the influence of Unnamed: 12 on Unnamed: 9
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Government

Start A New Notebook!

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

This data set comprises of election returns gathered for the office of xxx at the county/constituency/precinct level for the year(s) ... . Please see the associated readme file and meta-csv file for source information and important notes on accuracy and the nature of the fields.

April 7, 2020 - Spring Election

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This data set comprises of election returns gathered for the office of xxx at the county/constituency/precinct level for the year(s) ... . Please see the associated readme file and meta-csv file for source information and important notes on accuracy and the nature of the fields.

This csv is for the replication of the most up to date EPI data, 2020.

Following a curated dataset on Australia's Federal Election 2019, which has been published, the QUT Digital Observatory along with Professor Axel Bruns, Professor Daniel Angus, and PhD student Tegan Cohen carried out another collection of tweets from Twitter accounts officially associated with Queensland election candidates in 2020.

Files included:

qldelection2020_candidate_tweets.csv: Full text and metadata of tweets posted by election candidates during the campaign.
queenslandelection2020conversationtweetids.csv: tweet IDs of all tweets in the collection, including tweets from the broader community mentioning the candidate accounts. Full text cannot be provided openly for tweets not posted by the election candidates. If you wish to work with the full data of these tweets, please contact the QUT Digital Observatory to discuss access.
candidate_info.csv: Names and party affiliations of included candidates.
queensland-election-2020-candidates-dataset.pdf: background info and summary data for the candidate tweet data (corresponding to qldelection2020_candidate_tweets.csv).
queensland-election-2020-conversation-dataset.pdf: background info and summary data for the conversation data (corresponding to queenslandelection2020conversationtweetids.csv).

Electoral registrations for parliamentary and local government elections as recorded in electoral registers for England, Wales, Scotland and Northern Ireland.

November 3, 2020

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This graph shows the results of the 2016 presidential elections in the United States. Donald Trump has won the election with 306 votes in the electoral college. In the contested state of Florida he captured 49 percent of the vote.

Has the “big lie†—the false claim that the 2020 election was stolen from Donald Trump—shaped citizens’ views of the legitimacy of other U.S. elections? We argue that it has. Those who believe Trump’s claim, whom we call election skeptics, lack confidence in elections for two inter-related reasons. First, because they think 2020 was inaccurately and unfairly conducted, they think that other elections will suffer a similar fate, and hence think these elections are illegitimate even before any votes are cast. Second, while all voters think elections are less legitimate when their preferred candidate loses, this effect will be especially large for election skeptics, because voter fraud gives them a mechanism to explain their candidates’ loss. Using an original panel dataset spanning the 2020 and 2022 elections, we show strong support for these hypotheses. This has important implications for our elections, and their legitimacy, moving forward., , , # The Long Shadow of The Big Lie: How Beliefs about the Legitimacy of the 2020 Election Spill Over onto Future Elections

https://doi.org/10.5061/dryad.08kprr590

This file contains the codebooks, data, and R scripts necessary to replicate the results of "The Long Shadow of the Big Lie."

Description of the data and file structure

Data are sorted by Figure and Table. Each has its respective Codebook.docx, .sav & .csv data files, and _Replication.R scripts.

R and the haven package are both open-source and will allow all researchers the ability to replicate the analyses without losing variable labels and other data information in the .sav files. The .csv files are direct conversions, but lack some meta data that may be helpful with replication.

NAs in the original variables represent panelists who did not participate in that wave of the panel. Codes 997 - 999 represent Not Sure, Don't Know, or Skipped Items. 997-998 are often recoded...

This data set comprises of election returns gathered for the office of xxx at the constituency level for the years 1976 - 2020. Please see the associated readme file and meta-csv file for source information and important notes on accuracy and the nature of the fields.

This data set contains machine-readable data on the district representation election on September 13, 2020 in Münster. If you are looking for visually prepared results or an interactive presentation, you can find them at the following link: https://wahlen.citeq.de/ergebnisse-1/20200913/05515000/html5/index.html Following the municipal territorial reform in 1975, the state of North Rhine-Westphalia legally required the district-free cities to form district representations in order to strengthen civic participation and civic work in self-government. The district councils decide on the matters of their districts. These include, for example, the entertainment and equipment of local schools, sports grounds or cemeteries, the construction and renovation of playgrounds, the design of parks and green areas, the naming of streets and squares, issues of monument protection and the support of local associations and initiatives. If a matter concerns the district, the district representation concerned must be consulted. It can forward suggestions and decision proposals to the decisive body. The district representation must in any case be informed about essential measures in the municipal district. The CSV files have the following columns: date: Date of election date choice: Name of the election AGS: AGS of the Authority area-nr: Number of the electoral area area name: Name of the electoral area Max Quick Messages: Number of expected rapid notifications in the electoral area ANZ Quick Messages: Number of fast notifications received so far in the electoral area A1: Eligible voters without blocking mark ‘W’ A2: Eligible voters with blocking mark ‘W’ A3: Eligible voters not in the electoral register A: Total Electoral Electoral B: Voters B1: Voters with an Election Certificate C: Invalid votes D: Valid votes D1: Votes for Party 1 ...: ... D99: Vote for Party 99

QUT Research Data Respository Dataset Resource available for download

→ Motivations for creating the dataset Crossing data with political color may be interesting in many areas of analysis. However, there is now the National Register of Elected Representatives (RNE) which lists all elected officials by mandate, but it does not inform their political party(ies). This published dataset therefore corresponds to the municipal NER (mayors only) enriched with the political color retrieved from the results of the municipal elections in rounds 1 and 2, available on OpenDataSoft. → Composition of the dataset The database consists of the following fields: - municipal_name: name of the municipality - cog_commune: Official Geographic Code (COG) of the municipality (also known as the INSEE code) - siren_commune: SIREN number of the municipality - name_firstname_mayor: NAME and first name of the mayor of the municipality - political_nuance: acronym for the political nuance of the mayor - nuance_family: political nuance family, created from the circular on the attribution of political nuances to candidates in the municipal and community elections of 15 and 22 March 2020 The data is available in CSV format encoded in UTF-8, with a comma separator. → Data collection and processing process The data are extracted from the NER file "_elus-conseillers-municipaux-cm.csv_" available at datagouv. These data were processed with the following steps before being enriched with the political colour: - filter of the wording of the elected official's position on mayors only - creation of a column gathering the COG of the municipality with the name and surname of the mayor in the format 'NAME First name', which will be used to join (to avoid recovering several political parties when several elected officials have the same name and surname) The data of the political colour come from the results of the municipal elections, turn 1 and turn 2. They were processed with the following steps before being attached to the NER data: - gathering of the 2 games in 1 - creation of a column gathering the COG of the municipality with the names and surnames of the candidates in the format 'NAME First name' The 2 datasets could thus be joined by the COG column and surname / first name, allowing to retrieve the code of the political nuance from the results of the municipal elections of 2020. The nuance family was also created from the circular Légifrance, when mayors have 2 political colors and they are not part of the same family, only the first one was considered to assign the political color, for the sake of simplifying the output data. Finally, the SIREN number of the municipality has been added from the dataset "_Identifiers of local and regional authorities and their establishments_" available on datagouv, to allow the enrichment of this dataset published with different external sources. The data extraction and processing script is available at this link. → Dissemination of the dataset The dataset is published on the data.gouv.fr portal with the Datactivist account under Open License as the files used for its creation. To quote this dataset, indicate: Source Datactivist (2024-11-08) → Dataset maintenance Ideally, the update should be done at each municipal election. Datactivist does not undertake to carry out this update, but makes available scripts to generate new versions. If you have any questions or problems, please contact diane@datactivist.coop or post a comment below.

Data Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use it only for research purposes. Due to these restrictions, the collection is not open data. Please download the Agreement at Data Sharing Agreement and send the signed form to fakenewstask@gmail.com .

Citation

Please cite our work as

@article{shahi2021overview,
 title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
 author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
 journal={Working Notes of CLEF},
 year={2021}
}

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English.

Subtask 3A: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 900 articles with the respective label. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:

• False - The main claim made in an article is untrue.

• Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

• True - This rating indicates that the primary elements of the main claim are demonstrably true.

• Other- An article that cannot be categorised as true, false, or partially false due to lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Subtask 3B: Topical Domain Classification of News Articles (English) Fact-checkers require background expertise to identify the truthfulness of an article. The categorisation will help to automate the sampling process from a stream of data. Given the text of a news article, determine the topical domain of the article (English). This is a classification problem. The task is to categorise fake news articles into six topical categories like health, election, crime, climate, election, education. This task will be offered for a subset of the data of Subtask 3A.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

Task 3a

• ID- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• our rating - class of the news article as false, partially false, true, other

Task 3b

• public_id- Unique identifier of the news article
• Title- Title of the news article
• text- Text mentioned inside the news article
• domain - domain of the given news article(applicable only for task B)

Output data format

Task 3a

• public_id- Unique identifier of the news article
• predicted_rating- predicted class

Sample File

public_id, predicted_rating
1, false
2, true

Task 3b

• public_id- Unique identifier of the news article
• predicted_domain- predicted domain

Sample file

public_id, predicted_domain
1, health
2, crime

Additional data for Training

To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible source:

• Fakenews Classification Datasets
• Fake News Detection Challenge KDD 2020
• FakeNewsNet

IMPORTANT!

1. Fake news article used for task 3b is a subset of task 3a.
2. We have used the data from 2010 to 2021, and the content of fake news is mixed up with several topics like election, COVID-19 etc.

Evaluation Metrics

This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. There is a limit of 5 runs (total and not per day), and only one person from a team is allowed to submit runs.

Submission Link: https://competitions.codalab.org/competitions/31238

Related Work

• Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
• G. K. Shahi and D. Nandini, “FakeCovid – a multilingualcross-domain fact check news dataset for covid-19,” inWorkshop Proceedings of the 14th International AAAIConference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
• Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

ActBlue 2019 Year End Analysis

This repository contains curated bulk data and sample analysis provided by Circa Victor. Please feel free to share and report on this information as you see fit. All we ask is that you please cite us in your investigative journals. Our goal is to deliver actionable data through increased comprehension and quality analysis.

Need more in-depth analysis? Email us at team@circavictor.com.

By the Numbers

Committee: ActBlue

FECfile: 1378435

filed: January 31st, 2020

size: ~ 9.7gb

data points: 44,627,333

contributions: 24,656,453

volume: $525,124,217.30

contribution avg: $21.30

unique recipient committees: 1,844

unique individuals: 4,422,861

unique locations: 3,875,357

unique zipcodes: 39,359

Analysis

• Principal Committees by total
• Principal Committees by avg
• Individual contributors by total
• Individual contributors by average >= 5 contributions
• Recipient Committees by total
• Recipient Committees by average
• States by overall total
• States by average
• Zipcodes by overall total
• Zipcodes by average >= 250 contributions
• Direct contributions to ActBlue
• Future nominee contributions
• Form types breakdown

Why?

There currently exists a HUGE gap in accurate timely reporting around all things Campaign Finance. We feel that it's every citizen's right to be accurately informed in a timely manner. Open sourcing this analysis and dataset is a public statement to that commitment.

Who?

Hello, nice to meet you, we are team Circa Victor.

Looking for deeper analysis? Contact us at team@circavictor.com.

Data Breakdown

For quicker reporting and easier consumation, the data has been broken down into separate csv files by state. Take a look at the state volume analysis to see how many transactions are available for your state.

fyi: in order to reduce the csv file size, recipient committees have been abstracted down to their FEC id.

• Contributions by State
  • individual id - contributor individual id
  • location id - the contribution origin location. multiple individuals may be at the same location
  • first name - contributor's first initial
  • last name - contributor's last
  • amount - contribution amount
  • contributed date - the date that this contribution was made
  • recipient committee fec id - the FEC committee id of the
  • transction id - the transaction id filed by ActBlue
  • contribution aggregate - total aggregate this individual has given
  • memo - memo text for the contribution. may contain additional insights
  • zip code - the zipcode where contribution originated
  • state - the state where the contribution came from
• Recipient Committees
  • fec id - the committee FEC id. you can match this field to the recipient commitee fec id in state contributions
  • name - committee name
  • candidate fec id - if attached to a candidate, the candidate's FE...