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

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

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

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

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

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

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

This dataset contains detailed Facebook advertising data for the political party Sumar and its leader Yolanda Díaz for the period from 9 January to 21 July 2023. This timeframe includes the campaign and pre-campaign periods of the 2023 Spanish general election. The dataset provides a comprehensive overview of the party's advertising strategies on Facebook, including unique ad IDs from Facebook's ad library, average cost per ad, dates, ad text, links, ad categories and estimated reach segmented by age, gender and geography. The dataset also includes information on the languages used in the ads, providing insights into the party's targeting and communication approaches during this crucial election period.

New York City Board of Elections election district boundaries for New York City including portions under water. These district boundaries represent the redistricting as of the US Census 2020.

Welcome to BatchData, your trusted source for comprehensive US homeowner data, contact information, and demographic data, all designed to empower political campaigns. In the fast-paced world of politics, staying ahead and targeting the right audience is crucial for success.

At BatchData, we understand the importance of having the most accurate, up-to-date, and relevant data to help you make informed decisions and connect with your constituents effectively. With our robust data offerings, political campaign agencies can easily reach both homeowners and renters, using direct contact information such as cell phone numbers, emails, and mailing addresses.

The Power of Data in Political Campaigns In the digital age, political campaigns are increasingly reliant on data-driven strategies. Precise targeting, tailored messaging, and efficient outreach have become the cornerstones of successful political campaigning. BatchData equips political campaign agencies with the tools they need to harness the power of data in their campaigns, enabling them to make the most of every interaction. Harness the power of voter data and campaign & election data to effectively run political campaigns.

Key Features of BatchData 1. US Homeowner Data At BatchData, we understand that having access to accurate and comprehensive homeowner data is the bedrock of a successful political campaign. Our vast database includes information on homeowners across the United States, allowing you to precisely target this key demographic. With our homeowner data, you can segment your campaign and craft messages that resonate with this audience. Whether you're running a local, state, or national campaign, our homeowner data is an invaluable asset.

1. Contact Information 258M Phone Numbers (US Phone Number Data) BatchData doesn't just stop at providing you with demographic data; we go a step further by giving you direct contact information. We offer cell phone numbers, email addresses, and mailing addresses, ensuring that you can connect with your audience on multiple fronts. This multifaceted approach allows you to engage with potential voters in a way that suits their preferences and lifestyles. Whether you want to send targeted emails, reach out through phone calls, or even send physical mailers, BatchData has you covered with both the data and the tools. (See BatchDialer for more Info).

2. Demographic Data In addition to homeowner data and contact information, BatchData provides a treasure trove of demographic data. You can refine your campaign strategy by tailoring your messages to specific demographics, including age, gender, income, religious preferences, and more. Our demographic data helps you understand your audience better, allowing you to craft compelling messages that resonate with their values and interests.

3. Targeting Both Homeowners and Renters We understand that not all political campaigns are exclusively focused on homeowners. That's why BatchData caters to a diverse range of campaign needs. Whether your campaign is directed at homeowners or renters, our data sets have you covered. You can effectively target a broader spectrum of the population, ensuring that your message reaches the right people, regardless of their housing status.

Flexible Data Delivery Methods BatchData understands that political campaigns are time-sensitive, and efficiency is paramount. That's why we offer a variety of data delivery methods to suit your specific needs.

1. API Integration For real-time access to data, our API integration is your go-to solution. Easily integrate BatchData's data into your campaign management systems, ensuring that you always have the latest information at your fingertips.

2. Bulk File Delivery When you require a large volume of data in one go, our bulk file delivery option is ideal. We'll deliver the data to you in a format that's easy to import into your campaign databases, allowing you to work with a comprehensive dataset on your terms.

3. S3 Data Storage If you prefer to host your data in an S3 bucket, BatchData can seamlessly deliver your datasets to the cloud storage location of your choice. This option ensures that your data is readily available whenever you need it.

4. Self-Service List Building Our self-service list building tool empowers you to create custom lists based on your specific criteria. You have the flexibility to choose the data elements you need, ensuring that your campaign efforts are tailored to your goals.

5. File Exporting Need to download data for offline use or share it with your team? Our file exporting feature lets you export data in a user-friendly format, making it easy to work with.

6. On-Demand Concierge Services For those campaigns that require a more personalized touch, BatchData offers on-demand concierge services. Our experienced team is here to assist you in building lists, refining your targeting, and providing support as needed. This ...

Architect of the Capitol jurisdiction boundary. The dataset contains locations and attributes of the Architect of the Capitol jurisdiction boundary, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. The boundary was identified from public records (including the Architect's http://www.aoc.gov/cc/cc_map.htm ) and heads-up digitized using a combination of the 1995/1999 orthophotographs and planimetric roads features.

Political districts of the city of Trier (Polygon):Border of the city of Trier

Political townships are those with elected officials - township supervisor, assessor, road commissioner, etc. However, Zion Township is uniquely defined in law as being coterminous with the City of Zion. Since the city can and does annex additional land, whenever it does so the boundary of Zion Township also changes to match it.

Update Frequency:
This dataset is updated on a weekly basis.

Using the Twitter Search API, we collected all tweets posted by official MC accounts (voting members only) during the 115th U.S. Congress which ran January 3, 2017 to January 3, 2019. We identified MCs' Twitter user names by combining the lists of MC social media accounts from the United States project (https://github.com/unitedstates/congress-legislators), George Washington Libraries (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UIVHQR), and the Sunlight Foundation (https://sunlightlabs.github.io/congress/index.html#legislator-spreadsheet). Throughout 2017 and 2018, we used the Twitter API to search for the user names in this composite list and retrieved the accounts' most recent tweets. Our final search occurred on January 3, 2019, shortly after the 115th U.S. Congress ended. In all, we collected 1,485,834 original tweets (i.e., we excluded retweets) from 524 accounts. The accounts differ from the total size of Congress because we included tweet data for MCs who resigned (e.g., Ryan Zinke) and those who joined off cycle (e.g., Rep. Conor Lamb); we were also unable to confirm accounts for every state and district.Twitter prohibits us from sharing the full tweet text, and so we have included tweet IDs when possible.

Political Points in the Geographic Names Information System (GNIS)This feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays political points from the Geographic Names Information System (GNIS). Per USGS, “the Geographic Names Information System (GNIS) is the federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types.”Political Points of Populated Places, Civil, Forest, Parks, and ReservesData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (geonames) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: Geographic Names Information System (GNIS) - USGS National Map Downloadable Data CollectionGeoplatform: Geographic Names Information System (GNIS) - USGS National Map Downloadable Data CollectionOGC API Features Link: (Political Points in the Geographic Names Information System (GNIS) - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: U.S. Board on Geographic NamesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Cultural Resources Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cultural Resources are defined as "features and characteristics of a collection of places of significance in history, architecture, engineering, or society. Includes National Monuments and Icons."For other NGDA Content: Esri Federal Datasets

2022 Brazilian Presidential Election

This dataset contains 13,910,048 tweets from 1,346,340 users, extracted using 157 search terms over 56 different days between January 1st and June 21st, 2023.

All tweets in this dataset are in Brazilian Portuguese.

Data Usage

The dataset contains textual data from tweets, making it suitable for various NLP analyses, such as sentiment analysis, bias or stance detection, and toxic language detection. Additionally, users and tweets can be linked to create social graphs, enabling Social Network Analysis (SNA) to study polarization, communities, and other social dynamics.

Extraction Method

This data set was extracted using Twitter's (now X) official API—when Academic Research API access was still available—following the pipeline:

1. Twitter/X daily monitoring: The dataset author monitored daily political events appearing in Brazil's Trending Topics. Twitter/X has an automated system for classifying trending terms. When a term was identified as political, it was stored along with its date for later use as a search query. 2. Tweet collection using saved search terms: Once terms and their corresponding dates were recorded, tweets were extracted from 12:00 AM to 11:59 PM on the day the term entered the Trending Topics. A language filter was applied to select only tweets in Portuguese. The extraction was performed using the official Twitter/X API. 3. Data storage: The extracted data was organized by day and search term. If the same search term appeared in Trending Topics on consecutive days, a separate file was stored for each respective day.

Further Information

For more details, visit:

• The repository- Dataset short paper:

DOI: 10.5281/zenodo.14834434

Lobbyists registered with the Ethics Commission are required to file monthly electronic disclosure statements that include contact information, client lists, activity expenses, contacts of public officials, political contributions, and payments promised by clients.The contents of the electronic disclosure statements can be downloaded via an API. The API returns JSON from GET requests.See the source link for available methods.

2023 Brazilian Early Political Events

This dataset contains 13,910,048 tweets from 1,346,340 users, extracted using 157 search terms over 56 different days between January 1st and June 21st, 2023.

All tweets in this dataset are in Brazilian Portuguese.

Data Usage

The dataset contains textual data from tweets, making it suitable for various NLP analyses, such as sentiment analysis, bias or stance detection, and toxic language detection. Additionally, users and tweets can be linked to create social graphs, enabling Social Network Analysis (SNA) to study polarization, communities, and other social dynamics.

Extraction Method

This data set was extracted using Twitter's (now X) official API—when Academic Research API access was still available—following the pipeline:

1. Twitter/X daily monitoring: The dataset author monitored daily political events appearing in Brazil's Trending Topics. Twitter/X has an automated system for classifying trending terms. When a term was identified as political, it was stored along with its date for later use as a search query.

2. Tweet collection using saved search terms: Once terms and their corresponding dates were recorded, tweets were extracted from 12:00 AM to 11:59 PM on the day the term entered the Trending Topics. A language filter was applied to select only tweets in Portuguese. The extraction was performed using the official Twitter/X API.

3. Data storage: The extracted data was organized by day and search term. If the same search term appeared in Trending Topics on consecutive days, a separate file was stored for each respective day.

Further Information

For more details, visit:

- The repository
- Dataset short paper:

---

DOI: 10.5281/zenodo.14834704

The Maryland General Assembly, under the Maryland Constitution, enacted new congressional districts under SB 1012 (2022)​ based on the changes in population reported in the 2020 U.S. Census and adjusted in accordance with Maryland’s “No Representation Without Population Act” of 2010.​ ​This is a copy of the official layer clipped to the county boundary.

The Supplemental Nutrition Assistance Program (SNAP) is the second-largest and most contentious public assistance program administered by the United States government. The media forums where SNAP discourse occurs have changed with the advent of social and web-based media. We used machine learning techniques to characterize media coverage of SNAP over time (1990–2017), between outlets with national readership and those with narrower scopes, and, for a subset of web-based media, by the outlet’s political leaning. We applied structural topic models, a machine learning methodology that categorizes and summarizes large bodies of text that have document-level covariates or metadata, to a corpus of print media retrieved via LexisNexis (n = 76,634). For comparison, we complied a separate corpus via web-scrape algorithm of the Google News API (2012–2017), and assigned political alignment metadata to a subset documents according to a recent study of partisanship on social media. A similar procedure was used on a subset of the print media documents that could be matched to the same alignment index. Using linear regression models, we found some, but not all, topics to vary significantly with time, between large and small media outlets, and by political leaning. Our findings offer insights into the polarized and partisan nature of a major social welfare program in the United States, and the possible effects of new media environments on the state of this discourse.

The following dataset is prepared for the paper titled 'Fear-Anger Contests: Governmental and Populist Politics of Emotion'. The data gathering and cleaning processes are explained in detail in the Supplementary Information document present in this dataverse. This dataset contains the Tweet IDs of approximately 10.6 million tweets related to the 2016 Brexit Referendum in the UK (~0.87 million tweets) and 2016 United States presidential election of Donald Trump (~9.8 million tweets). They were collected between Jan 1, 2015 and Dec 31, 2020 from the Twitter API for Researchers. These Tweet IDs are presented into 11 collections. Each collection was collected from the GET statuses/user_timeline method of the Twitter REST API, following Twitter's Terms and Conditions. The collections are: Brexit Leave Political Actors: brexit_leave_pol_ids.txt Brexit Remain Political Actors: brexit_remain_pol_ids.txt Brexit News Media: brexit_media_ids.txt Brexit Society: brexit_soc_ids.txt US Democratic Political Actors: us_dem_pol_ids.txt US Republican Political Actors: us_rep_pol_ids.txt US News Media: us_media_ids.txt US Society (Elections): us_public_ele_ids.txt US Society (Biden): us_public_biden_ids.txt US Society (Clinton): us_public_clinton_ids.txt US Society (Trump): us_public_trump_ids.txt A README.txt file is also provided. The GET statuses/lookup method allows retrieving the complete Tweet (as well as full metadata: date, text, number of likes, retweets, etc.) for a Tweet ID. When retrieving information be aware that: Twitter may limit such a data retrieval. Please consult Twitter documentation. The Twitter API will not return tweets that have been deleted or belong to accounts that have been suspended, deleted, or made private. Therefore, several tweets may be unavailable. According to Twitter's documentation, duplicate tweets may appear when using filter streaming tools. Our dataset does not, however, contain duplicates. Following Twitter’s Developer Policy, Tweet IDs may be publicly shared; tweets' text and other information may not. Questions about this dataset can be sent to the authors.

Content

Obama Commutation

This folder contains data behind the story Obama Granted Clemency Unlike Any Other President In History.

The data in obama_commutations.csv is copied from the Justice Department website. The python script parses it by looking at the first column to figure out what is contained in the second column.

Source: Department of Justice

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

The voting precincts within Fairfax County.

Each Zip file contains all the bills introduced in that congress.So 93.Zip contains all bills in the 93rd congress.The data has been collected by ProPublica using APIs released by the US Congress.https://www.propublica.org/datastore/dataset/congressional-data-bulk-legislation-bills

Proportion for political chat posts and all chat posts are calculated by dividing the number of topics with the number of political topics, 45, and the total number of topics, 150.

The New Hampshire Political Boundaries (pbnh) layer provides a digital representation of corporate boundaries at the town level. It was derived from the 1:24,000-scale USGS Digital Line Graphs (DLGs).