Using Arabic Tweets to Understand Drug Selling Behaviors

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 Naïve Bayes, classifier yielded the highest accuracy rate (85%).

Prediction of depressed Arab women using their tweets

“…The formal Arabic language is much more complex than English, where one word can have many meanings, making it difficult for software to analyse at a high level of accuracy (Alghamdi et al, 2020). Moreover, the Arabic used is often informal, which has a high level of variance between each country and dialect (Alruwaili et al, 2019). This means that there would have to be a code for each Country, at times for each city.…”

Understanding and preventing the advertisement and sale of illicit drugs to young people through social media: A multidisciplinary scoping review

“…Other studies focused on the detection of illicit drug content using all the available text data within a post, not just keywords [47,51,52,[54][55][56]59,60,62]. This included full sentences, metadata (e.g., the number of times a post had been retweeted), the comments of the post and user information.…”

“…The removal of non-English words was also common in the processing stages, restricting posts by language. Only two papers did not focus on English but used text-classifications to detect tweets selling drugs in Arabic [52] and Spanish [61]. This signals the need for research from non-anglophone countries, to broaden understandings of diverse drug markets, social media platform structures and user behaviour, particularly given evidence of differentiated patterns across countries.…”