# Tracking Illicit Drug Dealing and Abuse on Instagram using Multimodal Analysis

arXiv: https://arxiv.org/abs/1605.02710 · HTML: https://mlsanity.com/paper/1605.02710/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2016.

## Built on by (most cited first, 6 of 6)

- [Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach](https://mlsanity.com/paper/2108.08920/) (2021)
- [Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion](https://mlsanity.com/paper/2108.08301/) (2021)
- [Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media](https://mlsanity.com/paper/2307.03699/) (2023)
- [Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis](https://mlsanity.com/paper/2311.04905/) (2023)
- [InstaIndoor and Multi-modal Deep Learning for Indoor Scene Recognition](https://mlsanity.com/paper/2112.12409/) (2021)
- [Tracking e-cigarette warning label compliance on Instagram with deep learning](https://mlsanity.com/paper/2102.04568/) (2021)

## Abstract (arXiv)

Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagram is of interest to law enforcement agencies and public health agencies. In this paper, we propose a novel approach to detecting drug abuse and dealing automatically by utilizing multimodal data on social media. This approach also enables us to identify drug-related posts and analyze the behavior patterns of drug-related user accounts. To better utilize multimodal data on social media, multimodal analysis methods including multitask learning and decision-level fusion are employed in our framework. Experiment results on expertly labeled data have demonstrated the effectiveness of our approach, as well as its scalability and reproducibility over labor-intensive conventional approaches.