SMM 4 H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from T witter: Machine Learning, Deep Learning, and Post-Processing (2020)

Abstract: This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.

## introduction substance abuse is a major public health crisis in the united states @xcite . use of natural language processing (nlp) for automatic detection of prescription abuse or misuse in social media posts holds promise as a toxicovigilance strategy for near real-time monitoring of prescription abuse patterns and emerging trends @xcite . research has shown that abuse-indicating social media posts correlate with opioid-related overdose deaths at a geographical level @xcite . despite its potential, social media poses significant challenges for detection of self-reported abuse or misuse of prescriptions, including colloquial use of drug names, sparse information, and figurative language. the social media mining for health applications (smm4h) task 4 challenges participants to classify tweets' mentions of opioids, benzodiazepines, atypical antipsychotics, central nervous system (cns) stimulants or gaba analogues as potential abuse/misuse (a), consumption (c), mention-only (m) or unrelated (u) @xcite . as patterns of abuse and misuse can vary based on drug class and mechanism of action, our team collaborated with clinical subject matter experts (smes) to complete the task. ## results and conclusion in this paper, we evaluate two systems as shown in