Social media text mining framework for drug abuse: Development and validation study with an opioid crisis case analysis

Abstract

Background: Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients' attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective: This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods: The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results: The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions: The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.