Two 1%s Don't Make a Whole: Comparing Simultaneous Samples from Twitter's Streaming API

By Kenneth Joseph, Peter Landwehr, Kathleen M. Carley. 2014

62 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Alexandra Olteanu
2. Carlos Castillo
3. Fernando Diaz
4. Emre Kıcıman
5. Sarah Vieweg
6. Muhammad Imran
7. Gabriella Kazai
8. Ricardo A. Baeza-Yates

In Statistical and Nonlinear Physics

1. Alexandra Olteanu
2. Carlos Castillo
3. Fernando Diaz
4. Emre Kıcıman
5. Sarah Vieweg
6. Muhammad Imran
7. Gabriella Kazai

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Further, in the case of Twitter, much research relies on APIs that give access to at most 1% of the data (González-Bailón et al., 2014a; Joseph et al., 2014; Morstatter et al., 2014).”
• Social media data for conservation science: A methodological overview (2019, background): “In comparison to APIs, web crawlers can only access the public web, while APIs may provide access to content that requires authentication (Lomborg and Bechmann, 2014).”
• What to Expect When the Unexpected Happens: Social Media Communications Across Crises (2015, background): “The quality of Twitter data samples acquired via the publicly available APIs that offer limited access to the full Twitter stream has been studied extensively, to understand the nature of the biases of such data samples [18, 19, 25, 31, 32].”