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Well curated, large-scale corpora of social media posts containing broadpublic opinion offer an alternative data source to complement traditionalsurveys. While surveys are effective at collecting representative samples andare capable of achieving high accuracy, they can be both expensive to run andlag public opinion by days or weeks. Both of these drawbacks could be overcomewith a real-time, high volume data stream and fast analysis pipeline. A centralchallenge in orchestrating such a data pipeline is devising an effective methodfor rapidly selecting the best corpus of relevant documents for analysis.Querying with keywords alone often includes irrelevant documents that are noteasily disambiguated with bag-of-words natural language processing methods.Here, we explore methods of corpus curation to filter irrelevant tweets usingpre-trained transformer-based models, fine-tuned for our binary classificationtask on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95.The low cost and high performance of fine-tuning such a model suggests that ourapproach could be of broad benefit as a pre-processing step for social mediadatasets with uncertain corpus boundaries.

Curating corpora with classifiers: A case study of clean energy sentiment online