Twitter analysis of the nonmedical use and side effects of methylphenidate: Machine learning study

Myeong Gyu Kim, Jungu Kim, Su Cheol Kim, Jaegwon Jeong

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Background: Methylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter. Objective: The purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach. Methods: A total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for “methylphenidate” and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects. Results: Of the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%). Conclusions: The SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.

Original languageEnglish
Article numbere16466
JournalJournal of Medical Internet Research
Volume22
Issue number2
DOIs
StatePublished - Feb 2020

Keywords

  • Drug-related side effects and adverse reactions
  • Machine learning
  • Methylphenidate
  • Prescription drug misuse
  • Social media
  • Support vector machine
  • Twitter

Fingerprint

Dive into the research topics of 'Twitter analysis of the nonmedical use and side effects of methylphenidate: Machine learning study'. Together they form a unique fingerprint.

Cite this