Twitter has captured the interest of the scientific community not only for its massive user base and content, but also for its openness in sharing its data. Twitter shares a free 1 sample of its tweets through the "Streaming API", a service that returns a sample of tweets according to a set of parameters set by the researcher. Recently, research has pointed to evidence of bias in the data returned through the Streaming API, raising concern in the integrity of this data service for use in research scenarios. While these results are important, the methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. In this work we tackle the problem of finding sample bias without the need for "gold standard" Firehose data. Namely, we focus on finding time periods in the Streaming API data where the trend of a hashtag is significantly different from its trend in the true activity on Twitter. We propose a solution that focuses on using an open data source to find bias in the Streaming API. Finally, we assess the utility of the data source in sparse data situations and for users issuing the same query from different regions.
The authors also compared topical facets of the text by extracting topics with LDA @cite_16 , where they found similar evidence of bias. The authors discover that the topics extracted through LDA are significantly different than those extracted from the gold standard Firehose data. The other facets compared in the data were the networks extracted from the dataset. Here, the authors extracted the User @math User retweet network from both sources and compare centrality measures across the two networks. They find that, on average, the Streaming API is able to find the most central users in the Firehose 50
@cite_16
1880262756
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.