Using Arabic Tweets to Understand Drug Selling Behaviors

TL;DR

This study analyzes Arabic tweets to identify illegal and legal drug selling behaviors, using machine learning classifiers to improve detection accuracy and support public health monitoring efforts.

Contribution

Introduces a machine learning approach to classify Arabic tweets related to drug sales and legality, enhancing social media monitoring for public health.

Findings

Support Vector Machine achieved 96% accuracy in detecting drug sale references.

Naive Bayes classifier achieved 85% accuracy in predicting drug legality.

The study demonstrates effective use of Arabic keywords and classifiers for drug-related social media analysis.

Abstract

Twitter is a popular platform for e-commerce in the Arab region including the sale of illegal goods and services. Social media platforms present multiple opportunities to mine information about behaviors pertaining to both illicit and pharmaceutical drugs and likewise to legal prescription drugs sold without a prescription, i.e., illegally. Recognized as a public health risk, the sale and use of illegal drugs, counterfeit versions of legal drugs, and legal drugs sold without a prescription constitute a widespread problem that is reflected in and facilitated by social media. Twitter provides a crucial resource for monitoring legal and illegal drug sales in order to support the larger goal of finding ways to protect patient safety. We collected our dataset using Arabic keywords. We then categorized the data using four machine learning classifiers. Based on a comparison of the respective results, we assessed the accuracy of each classifier in predicting two important considerations in analysing the extent to which drugs are available on social media: references to drugs for sale and the legality/illegality of the drugs thus advertised. For predicting tweets selling drugs, Support Vector Machine, yielded the highest accuracy rate (96%), whereas for predicting the legality of the advertised drugs, the Naive Bayes, classifier yielded the highest accuracy rate (85%).