Comparable 2022 General Election Advertising Datasets from Meta and Google
Abstract This paper introduces two comprehensive datasets containing information on digital ads in U.S. federal elections from Meta (including Facebook and Instagram) and Google (including YouTube) for the 2022 midterm general election period. We collected ads published on these platforms utilizing...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05228-w |
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| _version_ | 1850102450690719744 |
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| author | Meiqing Zhang Furkan Cakmak Markus Neumann Sebastian Zimmeck Pavel Oleinikov Jielu Yao Harry Yu Aleks Jacewicz Isabella Tassone Breeze Floyd Laura Baum Michael M. Franz Travis N. Ridout Erika Franklin Fowler |
| author_facet | Meiqing Zhang Furkan Cakmak Markus Neumann Sebastian Zimmeck Pavel Oleinikov Jielu Yao Harry Yu Aleks Jacewicz Isabella Tassone Breeze Floyd Laura Baum Michael M. Franz Travis N. Ridout Erika Franklin Fowler |
| author_sort | Meiqing Zhang |
| collection | DOAJ |
| description | Abstract This paper introduces two comprehensive datasets containing information on digital ads in U.S. federal elections from Meta (including Facebook and Instagram) and Google (including YouTube) for the 2022 midterm general election period. We collected ads published on these platforms utilizing their ad transparency libraries and web scraping techniques and added labels to make them more comparable. The collected data underwent processing to extract audiovisual and textual information through automatic speech recognition (ASR), face recognition, and optical character recognition (OCR). Additionally, we performed several classification tasks to enhance the utility of the dataset. The resulting datasets encompass a rich array of features, including metadata, transcripts, and classifications. These datasets provide valuable resources for researchers, policymakers, and journalists to analyze the digital election advertising landscape, campaign strategies, and public engagement. By offering detailed and structured data, our work facilitates diverse reuse possibilities in fields such as political science, communication studies, and data science, enabling comprehensive analysis and insights into the dynamics of digital political campaigns. |
| format | Article |
| id | doaj-art-e3871eef5de747e6879bf9347803379e |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-e3871eef5de747e6879bf9347803379e2025-08-20T02:39:45ZengNature PortfolioScientific Data2052-44632025-06-0112111410.1038/s41597-025-05228-wComparable 2022 General Election Advertising Datasets from Meta and GoogleMeiqing Zhang0Furkan Cakmak1Markus Neumann2Sebastian Zimmeck3Pavel Oleinikov4Jielu Yao5Harry Yu6Aleks Jacewicz7Isabella Tassone8Breeze Floyd9Laura Baum10Michael M. Franz11Travis N. Ridout12Erika Franklin Fowler13Wesleyan University, Wesleyan Media ProjectWesleyan University, Wesleyan Media ProjectDuke Kunshan University, Division of Social SciencesWesleyan University, Department of Mathematics and Computer ScienceWesleyan University, Hazel Quantitative Analysis CenterNational University of Singapore, East Asian InstituteWesleyan University, Department of Mathematics and Computer ScienceWesleyan University, Department of Mathematics and Computer ScienceWesleyan University, Department of Mathematics and Computer ScienceWesleyan University, Wesleyan Media ProjectWesleyan University, Wesleyan Media ProjectBowdoin College, Government and Legal StudiesWashington State University, School of Politics, Philosophy, and Public AffairsWesleyan University, Government DepartmentAbstract This paper introduces two comprehensive datasets containing information on digital ads in U.S. federal elections from Meta (including Facebook and Instagram) and Google (including YouTube) for the 2022 midterm general election period. We collected ads published on these platforms utilizing their ad transparency libraries and web scraping techniques and added labels to make them more comparable. The collected data underwent processing to extract audiovisual and textual information through automatic speech recognition (ASR), face recognition, and optical character recognition (OCR). Additionally, we performed several classification tasks to enhance the utility of the dataset. The resulting datasets encompass a rich array of features, including metadata, transcripts, and classifications. These datasets provide valuable resources for researchers, policymakers, and journalists to analyze the digital election advertising landscape, campaign strategies, and public engagement. By offering detailed and structured data, our work facilitates diverse reuse possibilities in fields such as political science, communication studies, and data science, enabling comprehensive analysis and insights into the dynamics of digital political campaigns.https://doi.org/10.1038/s41597-025-05228-w |
| spellingShingle | Meiqing Zhang Furkan Cakmak Markus Neumann Sebastian Zimmeck Pavel Oleinikov Jielu Yao Harry Yu Aleks Jacewicz Isabella Tassone Breeze Floyd Laura Baum Michael M. Franz Travis N. Ridout Erika Franklin Fowler Comparable 2022 General Election Advertising Datasets from Meta and Google Scientific Data |
| title | Comparable 2022 General Election Advertising Datasets from Meta and Google |
| title_full | Comparable 2022 General Election Advertising Datasets from Meta and Google |
| title_fullStr | Comparable 2022 General Election Advertising Datasets from Meta and Google |
| title_full_unstemmed | Comparable 2022 General Election Advertising Datasets from Meta and Google |
| title_short | Comparable 2022 General Election Advertising Datasets from Meta and Google |
| title_sort | comparable 2022 general election advertising datasets from meta and google |
| url | https://doi.org/10.1038/s41597-025-05228-w |
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