This Sample seems to be good enough! Assessing Coverage and Temporal Reliability of Twitter's Academic API
Not on the map. Why?
Abstract (arXiv)
Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for consulting businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to "access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets." The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2024Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenescited by 2
- 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in Englishcited by 2
- 2023Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)cited by 0
- 2023Sensemaking About Contraceptive Methods Across Online Platformscited by 0
- 2023The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twittercited by 0
- 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English11 Apr 2025
- 2024Improving Computer Vision Interpretability: Transparent Two-level Classification for Complex Scenes4 Jul 2024
- 2023The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twitter4 May 2023
- 2023Sensemaking About Contraceptive Methods Across Online Platforms23 Jan 2023
- 2023Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)20 Jan 2023