Optimizing Search API Queries for Twitter Topic Classifiers Using a Maximum Set Coverage Approach
This work addresses a practical bottleneck for researchers and developers using Twitter's limited API to apply topic classifiers, offering incremental improvements in query efficiency.
The paper tackles the problem of optimizing Twitter API queries for topic classifiers by proposing query optimization methods based on maximum coverage to minimize irrelevant data retrieval, showing that the best method significantly outperforms the firehose in precision and F1-score while maintaining high recall under API limits.
Twitter has grown to become an important platform to access immediate information about major events and dynamic topics. As one example, recent work has shown that classifiers trained to detect topical content on Twitter can generalize well beyond the training data. Since access to Twitter data is hidden behind a limited search API, it is impossible (for most users) to apply these classifiers directly to the Twitter unfiltered data streams ("firehose"). Rather, applications must first decide what content to retrieve through the search API before filtering that content with topical classifiers. Thus, it is critically important to query the Twitter API relative to the intended topical classifier in a way that minimizes the amount of negatively classified data retrieved. In this paper, we propose a sequence of query optimization methods that generalize notions of the maximum coverage problem to find the subset of query terms within the API limits that cover most of the topically relevant tweets without sacrificing precision. We evaluate the proposed methods on a large dataset of Twitter data collected during 2013 and 2014 labeled using manually curated hashtags for eight topics. Among many insights, our analysis shows that the best of the proposed methods can significantly outperform the firehose on precision and F1-score while achieving high recall within strict API limitations.