Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (1306.5204v1)

Abstract: Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.

• The paper compares the data coverage of Twitter’s Streaming API against the Firehose, revealing that it captures up to 43.5% of tweets with notable variability.
• It employs statistical measures and LDA topic modeling to show that while large hashtag trends align well, smaller subsets indicate sampling bias.
• Network and geographic analyses confirm that the Streaming API reliably identifies influential users and almost all geo-tagged tweets despite its limitations.

An Examination of Data Quality: Comparing Twitter's Streaming API to the Firehose

The paper "Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose" by Fred Morstatter, Juergen Pfeffer, Huan Liu, and Kathleen M. Carley evaluates the robustness and reliability of Twitter’s Streaming API when used as a data source in lieu of the more comprehensive but costly Firehose. Given the practical limitations associated with Firehose access, the Streaming API often serves as a primary data collection method for researchers. This paper scrutinizes the representativeness of the Streaming API against the Firehose, employing a range of methodological and empirical measures.

Data Collection and Coverage

Data was collected between December 14, 2011, and January 10, 2012, using both the Streaming API and the Firehose with identical parameters. The paper's analysis revealed that the Streaming API yielded, on average, 43.5% of the tweets obtained from the Firehose, significantly higher than the 1% sampling rate publicly documented by Twitter. However, this coverage fluctuated widely, suggesting dynamic factors influencing the sample size.

Statistical Analysis and Hashtag Correlation

The researchers employed statistical measures to compare both datasets, focusing initially on the alignment of trending hashtags. By using Kendall's $\tau$ rank correlation coefficient, they determined that the Streaming API correlates well with the Firehose for large sets of top hashtags (higher n values). However, inconsistencies arise when analyzing fewer top hashtags, potentially hinting at an intrinsic sampling bias within the Streaming API. Complementing this, random samples from the Firehose exhibited higher consistency, especially for smaller n values.

Topic Mining with Latent Dirichlet Allocation (LDA)

Topic modeling using LDA was conducted to better understand content representation. The Jensen-Shannon divergence was used to measure the deviation between topics derived from the two datasets. As expected, higher Streaming API coverage correlated with lower divergences, indicating closer topic similarity. Further comparisons with random samples demonstrated that LDA outputs from the Streaming API often diverged more significantly than those from random samples for identical coverage levels.

Network Analysis

Considering Twitter’s inherent social network structure, the paper assessed retweet networks through both node-level and network-level metrics. Key findings include:

• Node-Level Analysis: Significant consistency was observed in identifying influential users, with the Streaming API accurately identifying 50-60% of key players across daily retweet networks. This accuracy improved with longer observation periods.
• Network-Level Measures: Network metrics such as centralization, clustering coefficients, and the size of the largest connected component were analyzed. These metrics consistently reflected the proportional reduction in data sample size when moving from Firehose to Streaming API.

Geographic Analysis

Geo-tagged tweets represented another critical facet. Despite the Streaming API’s sampling, it captured almost the complete set of geo-tagged tweets, primarily influenced by the use of geographical bounding boxes in the collection parameters. This finding is significant given the relatively low proportion of geo-tagged tweets in the general Twitter dataset (~1%).

Implications and Future Directions

This paper highlights critical insights into the implications of relying on the Streaming API for research purposes. The inherent biases and varying coverage rates pose significant challenges. Nevertheless, the findings underscore the necessity for researchers to meticulously consider these limitations and, when possible, triangulate with the Firehose data for validation.

Future research could focus on refining methodologies to mitigate the biases presented by the Streaming API and develop techniques to estimate the coverage dynamically. Another avenue is to apply this paper's framework across different thematic datasets to evaluate its generalizability and robustness.

Overall, the paper provides a comprehensive, empirically-grounded scrutiny of the Streaming API’s validity as a reliable proxy for the Firehose, illuminating pathways for optimizing data collection strategies in social media research.