Assessing the Bias in Communication Networks Sampled from Twitter

By Sandra González-Bailón, Ning Wang, Alejandro Rivero, Javier Borge-Holthoefer, Yamir Moreno. 2012

55 citations recorded by the source corpus.

We collect and analyse messages exchanged in Twitter using two of the platform's publicly available APIs (the search and stream specifications). We assess the differences between the two samples, and compare the networks of communication reconstructed from them. The empirical context is given by political protests taking place in May 2012: we track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the two samples. We find that the search API over-represents the more central users and does not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions. We discuss the implications of this bias for the study of diffusion dynamics and collective action in the digital era, and advocate the need for more uniform sampling procedures in the study of online communication.

Subfields: Communication, Statistical and Nonlinear Physics

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Citations in the corpus

Two 1%s Don't Make a Whole: Comparing Simultaneous Samples from Twitter's Streaming API (2014, methodology): “In [8], the authors compare a sample from the Streaming API to one collected from the Search API(3), stating “[t]he alternative to comparing samples to the full stream of information is to compare the two available API specifications: streaming and search”.”
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].”
Twitter as Data (2018, background): “One study has found that results from GET search/tweets do not match those from the random sample (González-Bailón et al., 2012).”