# eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket

arXiv: https://arxiv.org/abs/2103.15764 · HTML: https://mlsanity.com/paper/2103.15764/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- ["When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction](https://mlsanity.com/paper/2009.10155/) (2020)
- ["Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision](https://mlsanity.com/paper/2102.01222/) (2021)
- [A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic](https://mlsanity.com/paper/1905.11513/) (2019)

## Built on by (most cited first, 3 of 3)

- [KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts](https://mlsanity.com/paper/2210.04307/) (2022)
- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)

## Abstract (arXiv)

Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the opioid crisis. The relationship between substance use and mental health has been extensively studied, with one possible relationship being substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance misuse posts on social media with the opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and BERT-based models to generate sentiment and emotion for the social media posts to understand user perception on social media by investigating questions such as, which synthetic opioids people are optimistic, neutral, or negative about or what kind of drugs induced fear and sorrow or what kind of drugs people love or thankful about or which drug people think negatively about or which opioids cause little to no sentimental reaction. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Our findings can help shape policy to help isolate opioid use cases where timely intervention may be required to prevent adverse consequences, prevent overdose-related deaths, and worsen the epidemic.