The Story of Goldilocks and Three Twitter’s APIs: A Pilot Study on Twitter Data Sources and Disclosure

Abstract

Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication. The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings. This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source.

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

“…The majority of these are now integrated into easy-to-use automatic kits available for Microsoft Excel software or similar (e.g., Real Statistics and Zaiontz, 2021 ; XLSTAT, 2021 ), which is a great advantage in terms of operational speed. However, when dealing with platforms such as Twitter, Reddit, Instagram, or Facebook, the collection and analysis of posts is still laborious: indeed, it requires the use of databases already extracted (which limits the power of investigation) or application programming interfaces (APIs) and all datasets must be suitably processed before use ( Kim et al, 2020 ). Therefore, while all of the above methods are essential and powerful for historical data analysis, more immediate and rapid tools are equally necessary for quasi-real-time infoveillance.…”

Mapping Population Vulnerability and Community Support during COVID-19

“…Publicly available data from Twitter were accessed via Twitter’s Streaming API [ 46 , 47 ] between 9th March and 15th June 2020, retrieving tweets whose Twitter place field was in Wales. The API returns a random sample of the total tweets from the specified area, up to a maximum of 1% of the total worldwide traffic [ 46 ]. The tweets returned by the API contain both the text of the tweet and associated meta-data.…”

Sweet tweets! Evaluating a new approach for probability-based sampling of Twitter

“…Recent research has compared the performance of samples gathered from each of these APIs with a focus on keywords, users, content, and Tweet volume (Tromble et al [ 26 ], Morstatter et al [ 18 ], Wang et al [ 27 ], Pfeffer et al [ 19 ], Kim et al [ 13 , 14 ]). Wang et al [ 27 ] verified that the Streaming API and Decahose produces samples that are approximately 1% and 10% of the entire Twitter corpus, but Pfeffer et al [ 19 ] provided cautionary evidence that samples from these APIs may not be random samples and may over-represent certain users or groups.…”