| NSF Org: |
SES Divn Of Social and Economic Sciences |
| Recipient: |
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| Initial Amendment Date: | August 7, 2022 |
| Latest Amendment Date: | August 7, 2022 |
| Award Number: | 2235006 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sara Kiesler
skiesler@nsf.gov �(703)292-8643 SES �Divn Of Social and Economic Sciences SBE �Direct For Social, Behav & Economic Scie |
| Start Date: | October 1, 2022 |
| End Date: | September 30, 2024�(Estimated) |
| Total Intended Award Amount: | $260,201.00 |
| Total Awarded Amount to Date: | $260,201.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
237 HIGH ST MIDDLETOWN CT �US �06459-3208 (860)685-3683 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
237 HIGH STREET RM 409 MIDDLETOWN CT �US �06459-3208 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Human Networks & Data Sci Infr, Secure &Trustworthy Cyberspace |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
Nearly $2 billion was spent on digital advertising in the presidential general election in 2020, and online advertising is only expected to grow. Campaigns? rapid shift to digital, however, has caused headaches for those who track political advertising. The data provided by online platforms, such as Facebook and Google, come in different formats, contain different collections that are not immediately comparable, and lack metadata necessary to find all ads related to a specific campaign for office, all ads featuring a specific candidate, or all ads within either set that discuss a specific issue. The sheer volume of content requires the use of computational methods to extract the information necessary to answer even basic descriptive questions such as how much money was spent online in a particular race or across all federal races. This project provides the infrastructure necessary to answer critical questions about the role of digital advertising in American democracy, including the extent to which ?dark money? dominates campaigns, and the spread and reach of misinformation in campaigns.
The project team is building expandable infrastructure to acquire, process, integrate, label, and distribute digital election advertising data from two large online platforms. The result is a centralized repository that provides robust documentation of all procedures and code such that they can be modified to apply to other platforms and contexts for expansion. Cross-platform integration and standardization of digital election advertising data through human and state-of-the-art computational methods reduces costs to individual researchers, provides parallel procedures already in place for analyzing TV advertising (and thus maximizing comparability to existing data), provides linkage information to other data sources, and produces accessible data to benefit the community. Accomplishing this aim is a high risk-high reward endeavor. For instance, new approaches or methods are needed to capture and label relevant advertising by focus, especially when the true sponsor is unclear or disguised.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Please report errors in award information by writing to: awardsearch@nsf.gov.
