Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

Tim Mackey; Janani Kalyanam; Josh Klugman; Ella Kuzmenko; Rashmi Gupta

doi:10.2196/10029

Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access (Preprint)

10.2196/preprints.10029 ◽

2018 ◽

Author(s):

Tim Mackey ◽

Janani Kalyanam ◽

Josh Klugman ◽

Ella Kuzmenko ◽

Rashmi Gupta

Keyword(s):

Machine Learning ◽

Illicit Drugs ◽

Topic Model ◽

Application Programming Interface ◽

Online Marketing ◽

Prescription Opioid ◽

Law Enforcement Agencies ◽

Controlled Substances ◽

The Us ◽

Social Media Platforms

BACKGROUND On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. OBJECTIVE The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. METHODS Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. RESULTS We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. CONCLUSIONS The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public.

Download Full-text

User Response Prediction in Online Advertising

ACM Computing Surveys ◽

10.1145/3446662 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-43

Author(s):

Zhabiz Gharibshah ◽

Xingquan Zhu

Keyword(s):

Machine Learning ◽

Mobile Apps ◽

Online Advertising ◽

Response Prediction ◽

Online Marketing ◽

Data Availability ◽

Third Party ◽

Learning Methods ◽

Comprehensive Review ◽

Machine Learning Methods

Online advertising, as a vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How do we predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open source codes in the field.

Download Full-text

Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC–MS-MS Machine Learning Models and the Hybrid Similarity Search Algorithm

Journal of Analytical Toxicology ◽

10.1093/jat/bkab098 ◽

2021 ◽

Author(s):

So Yeon Lee ◽

Sang Tak Lee ◽

Sungill Suh ◽

Bum Jun Ko ◽

Han Bin Oh

Keyword(s):

Machine Learning ◽

High Resolution ◽

Carbonyl Compound ◽

Similarity Search ◽

New Psychoactive Substances ◽

Support Vector ◽

Learning Models ◽

Psychoactive Substances ◽

Controlled Substances ◽

Machine Learning Models

Abstract High-resolution liquid chromatography (LC)–tandem mass spectrometry (MS-MS)-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPSs). Using a training set composed of 770 LC–MS-MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPSs were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine and others). Using 193 LC–MS-MS barcode spectra as an external test set, accuracy of the ANN, SVM and k-NN models were evaluated as 72.5%, 90.0% and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPSs whose data are unavailable in the database. When only 24 representative LC–MS-MS spectra of controlled substances and NPSs were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded artificial intelligence screener for narcotic drugs and psychotropic substances standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPSs to be identified in a convenient manner.

Download Full-text

An Innovative Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Project Finalist at HHS Opioid Code-a-Thon

10.2196/preprints.10029.a ◽

2018 ◽

Author(s):

Tim Mackey ◽

Janani Kalyanam ◽

Josh Klugman ◽

Ella Kuzmenko ◽

Rashmi Gupta

Keyword(s):

Online Marketing ◽

Controlled Substances ◽

Innovative Solution

Download Full-text

Light GBM Machine Learning Algorithm to Online Click Fraud Detection

Journal of Information Assurance & Cybersecurity ◽

10.5171/2019.263928 ◽

2019 ◽

pp. 1-12

Author(s):

Elena-Adriana MINASTIREANU ◽

Gabriela MESNITA

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Online Advertising ◽

Fraud Detection ◽

Online Marketing ◽

Machine Learning Algorithm ◽

Advertising Industry ◽

Click Fraud ◽

Web Advertising ◽

Critical Issues

In the current web advertising activities, the fraud increases the number of risks for online marketing, advertising industry and e-business. The click fraud is considered one of the most critical issues in online advertising. Even if the online advertisers make permanent efforts to improve the traffic filtering techniques, they are still looking for the best protection methods to detect click frauds.

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text