Dataset

This dataset was created by SUJIB BARMAN

Released under Apache 2.0

Contents

Please read the collection notes below; there are many points to be aware of for this collection prior to analysis. This collection of historical election data contains state files that list county-level returns for over 90 percent of all elections to the offices of president, governor, United States senator, and United States representative from 1824 through 1968. The data files include returns for all parties and candidates (as well as write-in and scattering votes if available for individual states), and for special elections as well as regularly-scheduled contests. Over 1,000 individual party names and many additional unaffiliated candidates are included.

The Dataset contains constituency type and gender-wise Nominations Withdrawn, Forfeited Deposits, Contesting Candidates, Nominations Filed, Nominations Rejected in the assembly elections for each state

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

This dataset was used to conduct the NYC Campaign Finance Board's voter participation research, published in the 2019-2020 Voter Analysis Report. Each row contains information about an active voter in 2018 and their voting history dating back to 2008, along with geographical information from their place of residence for each year they were registered voters. Because this dataset contains only active voters in the year 2018, this dataset cannot be used to calculate election turnout.

Dataset for General Election 2024

Plots :

1. Difference between EVM Votes counted and EVM votes polled across PCs 🔗
2. Victory Margins and all related information 🔗

Files : 1. eci_data_2024.csv - Raw Data scraped from Election Commission Of India Results 2024 2. phase_data.xlsx - Election Commission of India Press Releases (Phase 1 - 5 , Phase 6 and Phase 7) 3. GE India 2024 - Reconciled Data (Difference of EVM Votes counts and EVM Votes Polled, Victory Margins)

dataset contains:

The dataset was created for a sentiment analysis project focusing on the 2024 Lok Sabha elections in India. The aim was to understand public sentiment towards Narendra Modi and the BJP government based on comments gathered from online platforms.

The primary data source for this project is YouTube comments. The comments were collected from various YouTube videos related to Indian politics, election campaigns, and speeches by political figures. The comments were then processed and structured to create a dataset suitable for sentiment analysis.

The inspiration behind this dataset stems from the importance of understanding public opinion and sentiment in political decision-making processes. By analyzing sentiments expressed in online discussions and comments, this project seeks to gain insights into the public's perception of political leaders and parties, particularly in the context of elections. The ultimate goal is to leverage sentiment analysis to make informed predictions and draw conclusions about the potential outcomes of the 2024 Lok Sabha elections.

Voting Behavior, The 2016 Election is an instructional module designed to offer students the opportunity to analyze a dataset drawn from the American National Election (ANES) 2016 Time Series Study [ICPSR 36824]. This instructional module is part of the SETUPS (Supplementary Empirical Teaching Units in Political Science) series and differs from previous modules in that it is completely online, including the data analysis system components.

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.

This study uses prediction market data from the nation’s historical election betting markets to measure electoral competition in the American states during the era before the advent of scientific polling. Betting odds data capture ex ante expectations of electoral closeness in the aggregate, and as such improve upon existing measures of competition based on election returns data. Situated in an analysis of the1896 presidential election and its associated realignment, I argue that the market odds data show that people were able to anticipate the realignment and that expectations on the outcome in the states influenced voter turnout. Findings show that a month ahead of the election betting markets accurately forecast a McKinley victory in most states. This study further demonstrates that the market predictions identify those states where electoral competition would increase or decline that year and the consequences of these expected partisanship shifts on turnout. In places where the anticipation was for a close race voter expectations account for a turnout increase of as much as 6%. Participation dropped by 1% to 6% in states perceived as becoming electorally uncompetitive. The results support the conversion and dealignment theories from the realignment literature.

This study consists of a content analysis of 12 different campaign information sources from the 1996 presidential election campaign. Each communication outlet (or medium) was analyzed using a consistent set of guidelines so the various kinds of media could be rigorously compared. The media include traditional news outlets, such as network and cable television news (CBS, ABC, NBC, CNN), news magazines (TIME magazine), newspapers (the NEW YORK TIMES and various other local newspapers), and political advertising, speeches, and debates. Other analyzed media include newer forms of campaign media, such as televised candidate interviews, candidate Web sites, and free televised candidate spots. Areas of investigation include type of news story, partisan focus of the story, control of the story, substantive focus of the story, election topics covered, sources of the story, issues revised, policy debate, reporting of the candidates' records and proposals, discussions of candidates' general political conduct, their personal background, and associates of the presidential candidates, and the political "horse race," in which reporters and sources assess the political strength or viability of each of the candidates. The information is presented in three types of files for each medium. The first file type is the Story-Level Data file, which is the master data file. It contains all of the variables coded, and the unit of analysis is the story, interview, ad, speech, etc. The second file type is the Multiple Story-Level Data file. In this file, topics span a number of units. For example, all issues mentioned in a "story" are coded, and each issue is considered a unit. This file contains the following coding blocks: polls, interview guests, topics, sources, issues, candidates' records, proposals and issue stands, sound bites, mentions of election-year players, correcting the record, and state contests/voting blocs. The third file type is the Statement-Level Data file. This file contains the following coding blocks: policy debate, evaluations of 1996 candidates, and horse race assessments. The unit of analysis in this file is the "statement," and each story is comprised of multiple statements. This file groups the statements by story and orders them by their placement within the story.

This dataset contains a financial summary of each campaign during an off-year or special election.

The database comprises a spatially referenced compilation of the results of the Russian State Duma elections by party lists in each Russian region. The base includes the results of the nationwide elections in 2007-2021, i.e. four electoral cycles. The statistics are attached to the vector cartographic base. The datased can be used for the complex spatial analysis of social and political processes, the research of factors of electoral geographic differenciation of the Russian space using various geoinformation systems (GIS) as well as the forcast of the elecrotal behaviour with the use of maps reflecting the influence of various factors on the electoral outcomes in each region.

CodeThis figshare repository hosts a collection of tools and scripts for Twitter data analysis, focusing on Election Prediction using sentiment analysis and tweet processing. The repository includes four key files:twitter_data_collection.py: This Python script is designed for collecting tweets from Twitter in JSON format. It provides a robust method for gathering data from the Twitter platform.EP.ipynb: EP.ipynb" is designed for sentiment analysis and tweet processing. It features three sentiment analysis methods: VADER, BERT, and BERTweet. It includes a US states dictionary for geolocating and categorizing tweets by state, providing sentiment analysis results in both volumetric and percentage formats. Furthermore, it offers time-series analysis options, particularly on a monthly basis. It also includes a feature for filtering COVID-19-related tweets. Additionally, it conducts election analysis at both state and country levels, giving insights into public sentiment and engagement regarding political elections.Datasetbiden and trump.csv Files:The "biden.csv" and "trump.csv" files together constitute an extensive dataset of tweets related to two prominent U.S. political figures, Joe Biden and Donald Trump. These files contain detailed information about each tweet, including the following key attributes:create_date: The date the tweet was created.id: A unique identifier for each tweet.tweet_text: The actual text content of the tweet.user_id: The unique identifier for the Twitter user who posted the tweet.user_name: The name of the Twitter user.user_screen_name: The Twitter handle of the user.user_location: The location provided by the user in their Twitter profile.state (location): The U.S. state associated with the user's provided location.text_clean: The tweet text after preprocessing, making it suitable for analysis.Additionally, sentiment analysis has been applied to these tweets using two different methods:VADER Sentiment Analysis: Each tweet has been assigned a sentiment score and a sentiment category (positive, negative, or neutral) using VADER sentiment analysis. The sentiment scores are provided in the "Vader_score" column, and the sentiment categories are in the "Vader_sentiment" column.BERTweet Sentiment Analysis: The files also feature sentiment labels assigned using the BERTweet sentiment analysis method, along with associated sentiment scores. The sentiment labels can be found in the "Sentiment" column, and the cleaned sentiment labels are available in the "Sentiment_clean" column.This combined dataset offers a valuable resource for exploring sentiment trends, conducting research on public sentiment, and analyzing Twitter users' opinions related to Joe Biden and Donald Trump. Researchers, data analysts, and sentiment analysis practitioners can utilize this data for a wide range of studies and projects.This repository serves as a resource for collecting, processing, and analyzing Twitter data with a focus on sentiment analysis. It offers a range of tools and datasets to support research and experimentation in this area.

This is partial replication data for a paper analyzing voting irregularities in Bolivia's 2019 election. This dataset includes municipal-level data for the following presidential elections: 2002, 2005, 2009, 2014, 2019, and 2020. The data includes voter turnout, MAS vote share, the share of the largest opposition party, effective number of parties, and select socioeconomic and demographic indicators.

This dataset contains detailed results of both Lok Sabha (Parliamentary) and Vidhan Sabha (State Legislative Assembly) elections in India. It includes information on constituencies, candidates, political parties, vote counts, and election outcomes across various years.
It is useful for data analysis, political research, trend visualization, and election prediction modeling.

Both datasets are provided together for easy comparison between national and state-level election trends.

Files in This Dataset

1. ind-lok-sabha.csv – Data from Lok Sabha elections.
2. ind-vidhan-sabha.csv – Data from Vidhan Sabha elections.

Columns Description

Common Columns

• State – Name of the state.
• Year – Election year.
• Constituency – Name of the constituency.
• Candidate – Name of the candidate.
• Party – Political party of the candidate.
• Total Votes – Total votes received by the candidate.
• Position – Rank of the candidate (1 for winner, 2 for runner-up, etc.).

Lok Sabha Specific Columns

• Parliament Constituency Code – Unique code for parliamentary constituency.

Vidhan Sabha Specific Columns

• Assembly Constituency Code – Unique code for assembly constituency.

Potential Uses

• Trend analysis of Indian elections.
• Political party performance studies.
• Predictive modeling for future elections.
• Data visualizations and dashboards.

Scholars are only beginning to understand the evolution of electoral sentiment over time. How do preferences come into focus over the electoral cycle in different countries? Do they evolve in patterned ways? Does the evolution vary across countries? This paper addresses these issues. We consider differences in political institutions and how they might impact voter preferences over the course of the election cycle. We then outline an empirical analysis relating support for parties or candidates in pre-election polls to their final vote. The analysis relies on over 26,000 vote intention polls in 45 countries since 1942, covering 312 discrete electoral cycles. Our results indicate that early polls contain substantial information about the final result but that they become increasingly informative over the election cycle. Although the degree to which this is true varies across countries in important and understandable ways given differences in political institutions, the pattern is strikingly general.

This Supplementary Empirical Teaching Units in Political Science (SETUPS) module is designed as an introduction to the study of elections, voting behavior, and survey data through the analysis of the 2000 United States general election. The data are taken from the AMERICAN NATIONAL ELECTION STUDY, 2000: PRE- AND POST-ELECTION SURVEY (ICPSR 3131), conducted by Nancy Burns, Donald R. Kinder, Steven J. Rosenstone, Virginia Sapiro, and the National Election Studies. A subset of items was drawn from the full election survey, including questions on voting behavior, political involvement, media involvement, candidate images, presidential approval and government performance, economic conditions, ideology, general spending and taxation, social welfare policy, foreign policy and defense issues, social and other domestic issues, civil rights and equality, and general orientations toward government. A number of social and demographic characteristics such as gender, race, age, marital status, education, occupation, income, religious affiliation, region, and employment status are also included.

These data include Zambian presidential election data, Zambian census data, and Afrobarometer data for Zambia between 2001 to 2016. Along with the data files is a STATA .do file that automatically preps the data files for analysis and runs all models and diagnostics. This analysis is conducted for the following manuscript:Presidential Election Outcomes and Related AfroBarometer Findings in Zambia’s Weakly Institutionalised Multi-Ethnic Party System, and Their Consequences for DemocracyAbstract: Presidential Elections in Zambia’s weakly institutionalised multi-ethnic party system involved three significant parties from 2006 until 2011 and two parties from then until now. The Movement for Multiparty Democracy was the winner until it collapsed in 2011, and the Patriotic Front has been in power since then. The United Party for National Development has finished 2nd or 3rd in all elections since 2001. We formulate four hypotheses and test them by analysing the role of ethnicity, urban-rural cleavages, and voter registration in these electoral outcomes using multi-level modelling. Next, we analyse AfroBarometer data on party support, collected at intervals between elections, and find that it often reports similar results. We find that all of these variables are significant in explaining party outcomes. Finally, we speculate why the current two-party system risks being undemocratic more than the former three-party system.

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.