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Political Advertising on Facebook During the 2022 Australian Federal Election

<p>This repository contains data on political advertisements posted on Facebook and Instagram during the three months leading up to the 2022 Australian federal election. The data was collected using the Meta Ad Library API and provides insights into digital political campaigning strategies in Australia.</p> <h2>Contents</h2> <p>The repository includes the following files:</p> <ul> <li><code>datasheet.txt</code>: A detailed datasheet following the guidelines of Gebru et al. (2021), providing transparency about data collection, preprocessing, and handling procedures.</li> <li><code>party_information.csv</code>: Manually annotated data matching candidates, electorates, and parties based on ad funding entity information.</li> <li><code>maps_australia.zip</code>: A set of Australian maps used for visualizing the geographical distribution of ads during the campaign period.</li> <li><code>2022_AU_election_raw.zip</code>: Raw data collected from the Meta Ad Library API, containing comprehensive information on political ads run on Facebook and Instagram in Australia during the specified period.</li> <li><code>keywords.csv</code>: A curated list of keywords relevant to the 2022 Australian federal election, useful for content analysis and topic modeling of the advertisements.</li> </ul> <h2>Related Code Repository</h2> <p>The code used to analyze this data and reproduce the results presented in the paper "Political Advertising on Facebook During the 2022 Australian Federal Election" can be found in a separate repository:</p> <p><a href="https://anonymous.4open.science/r/Political-Advertising-2022-AU-Federal-Election-3E75/README.md">https://anonymous.4open.science/r/Political-Advertising-2022-AU-Federal-Election-3E75/README.md</a></p> <h2>References</h2> <p>Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., &amp; Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86&ndash;92.</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0