A comprehensive understanding of data bias is the cornerstone of mitigating biases in social media research. This paper presents in-depth measurements of the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing two complete tweet streams, we show that Twitter rate limit message is an accurate measure for the volume of missing tweets. Despite sampling rates having clear temporal variations, we find that the Bernoulli process with a uniform rate well approximates Twitter data sampling, and it allows to estimate the ground-truth entity frequency and ranking with the observed sample data. In terms of network analysis, we observe significant structure changes in both the user-hashtag bipartite graph and the retweet network. Finally, we measure the retweet cascades. We identify risks for information diffusion models that rely on tweet inter-arrival times and user influence . This work calls attention to the social databias caused by data collection, and proposes methods to measure the systematic biases introduced by sampling .
A comprehensive understanding of data quality is the cornerstone of measurement studies in social media research. This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing complete tweet streams, we show that Twitter rate limit message is an accurate indicator for the volume of missing tweets. Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find the Bernoulli process with a uniform rate approximates the empirical distributions well. It also allows us to estimate the true ranking with the observed sample data. For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features . This work calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects .
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Despite sampling rates having clear temporal variations, we find that
Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find
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well approximates Twitter data sampling, and it allows
approximates the empirical distributions well. It also allows us
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ground-truth entity frequency and
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In terms of network analysis, we observe significant structure changes in both the user-hashtag bipartite graph and the retweet network. Finally, we measure the retweet cascades. We identify risks for information diffusion
For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect
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tweet inter-arrival times and user influence
these features
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the social databias caused by data collection, and proposes methods to measure the systematic biases introduced by sampling
noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects