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The Upworthy Research Archive

The Upworthy Research Archive

Stefan
Open Dataset
Creators: The Upworthy Research Archive
Publication Date: 2019
Creators: The Upworthy Research Archive

The Upworthy Research Archive is an open dataset of thousands of A/B tests of headlines conducted by Upworthy from January 2013 to April 2015. This repository includes the full data from the archive. The dataset’s size is approximately 149,7 MB. It includes 32,488 records of headline experiments, providing insights into how different headline variations impacted user engagement. The dataset is structured as a time series of experiments, with each record detailing the performance metrics of different headline variations. This structure enables researchers to analyze the effectiveness of various headlines and understand user engagement patterns over time.

Snap Political Ads Library

Snap Political Ads Library

Stefan
Open Dataset
Creators: Snapchat
Publication Date: 2024
Creators: Snapchat

The Political and Advocacy Ads Library is an important step in our efforts to increase the level of transparency around political and issue advertising on. The dataset includes advertisements from 2018 through 2025 with a total size of 538,8 kB. It offers detailed insights into political advertising on Snapchat, including:​

  • Ad Content: Access to the creative content of each advertisement through unique URLs.​

  • Financial Information: Details on the amount spent on each ad campaign, specified in the local currency.​

  • Impressions: Data on the number of times each ad was viewed by Snapchat users.​

  • Targeting Criteria: Information on the demographic and geographic targeting parameters used in each campaign.

These features enable researchers, policymakers, and the public to analyze the reach, expenditure, and strategies of political advertisers on Snapchat.

Facebook Ad Library

Facebook Ad Library

Stefan
Open Dataset
Creators: Franklin Fowler, Erika; Franz, Mike; King, Gary; Martin, Greg; Mukerjee, Zagreb; Persily, Nate
Publication Date: 2019
Creators: Franklin Fowler, Erika; Franz, Mike; King, Gary; Martin, Greg; Mukerjee, Zagreb; Persily, Nate

The Ad Library API provides programmatic access to the Facebook Ad Library, a collection of all political advertisements run on Facebook and Instagram since May 2018 in the US, and for other dates in different countries. The codebook describes the scope, structure, and fields of these data. The Ad Library offers detailed information about each advertisement, including:​

  • Ad Creative: Visual and textual content of the ad.​

  • Impressions: Number of times the ad was displayed.​

  • Spend: Estimated amount spent on the ad.​

  • Demographics: Age, gender, and location breakdown of the audience reached.

Given that the Ad Library archives all ads related to political content, social issues, and elections since May 2018, the number of observations runs into the millions. The Ad Library’s data is structured to include various attributes for each advertisement:​

  • Ad ID: Unique identifier for each ad.​

  • Page ID and Name: Information about the page running the ad.​

  • Ad Creative: Content and format of the ad.​

  • Impressions and Spend: Metrics indicating the ad’s reach and budget.​

  • Demographic Distribution: Breakdown of the audience by age, gender, and location.

​

Advertisement CTR Prediction Data

Advertisement CTR Prediction Data

Stefan
Open Dataset
Creators: Huawei
Publication Date: 2020
Creators: Huawei

Advertisement CTR prediction is the key problem in the area of computing advertising. Increasing the accuracy of Advertisement CTR prediction is critical to improve the effectiveness of precision marketing. In this competition, we release big advertising datasets that are anonymized. Based on the datasets, contestants are required to build Advertisement CTR prediction models. The aim of the event is to find talented individuals to promote the development of Advertisement CTR prediction algorithms. The datasets contain the advertising behavior data collected from seven consecutive days, including a training dataset and a testing dataset. The total size of the datasets amounts to 6,86 GB. It contains millions of observations and is structured into training and testing sets, with multiple variables capturing different aspects of user-ad interactions. These variables include user identifiers, ad identifiers, timestamps, user behavior features, and ad content features, allowing researchers to analyze engagement patterns and develop predictive models for ad click-through rates. This dataset is valuable for improving advertising strategies and refining targeted marketing approaches.

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