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Addressing Extreme Imbalance for Detecting Medications Mentioned in Twitter User Timelines

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12721)

Abstract

Tweets mentioning medications are valuable for efforts in digital epidemiology to supplement traditional methods of monitoring public health. A major obstacle, however, is to differentiate them from the large majority of tweets on other topics posted in a user’s timeline: solving the infamous ‘needle in a haystack’ problem. While deep learning models have significantly improved classification, their performance and inference processing time remain low on extremely imbalanced corpora where the tweets of interest are less than 1% of all tweets. In this study, we empirically evaluate under-sampling, fine-tuning, and filtering heuristics to train such classifiers. Using a corpus of 212 Twitter timelines (181,607 tweets with only 0.2% tweets mentioning a medication), our results show that combining these heuristics is necessary to impact the classifier’s performance. In our intrinsic evaluation, a classifier based on a lexicon and a BERT-base neural network achieved a 0.838 F1-score, a score similar to the score achieved by the best classifier on this task during the #SMM4H’20 competition, but it processed the corpus 28 times faster - a positive result, since processing speed is still a roadblock to deploying classifiers on large cohorts of Twitter users needed for pharmacovigilance. In our extrinsic evaluation, our classifier helped a labeler to extract the spans of medications more accurately and achieved a 0.76 Strict F1-score. To the best of our knowledge, this is the first evaluation of medications extraction from Twitter timelines and it establishes the first benchmark for future studies.

Keywords

Social Media Medication detection Text classification 

Notes

Funding

This work was supported by National Library of Medicine grant number R01LM011176 to GG-H. The content is solely the responsibility of the authors and does not necessarily represent the official view of National Library of Medicine.

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of Biostatistics, Epidemiology and Informatics (DBEI)University of PennsylvaniaPhiladelphiaUSA
  2. 2.School of Computing, Informatics, and Decision Systems Engineering (CIDSE)Arizona State UniversityTempeUSA

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