Using Arabic Tweets to Understand Drug Selling Behaviors
This work addresses public health risks from illegal drug sales on social media, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of monitoring drug sales on social media by analyzing Arabic tweets, achieving 96% accuracy in detecting drug-selling tweets and 85% accuracy in predicting drug legality using machine learning classifiers.
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%).