Tristan GreeneEditor, Neural by TNW
Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: He/him
An international team of researchers recently developed an AI system that pieces together bits of information from dark web cryptomarkets, Twitter, and Reddit in order to better understand substance abusers.
Don’t worry, it doesn’t track sales or expose users. It helps scientists better understand how substance abusers feel and what terms they’re using to describe their experiences.
The relationship between mental health and substance abuse is well-studied in clinical environments, but how users discuss and interact with one another in the real world remains beyond the realm of most scientific studies.
According to the team’s paper:
Recent results from the Global Drug Survey suggest that the percentage of participants who have been purchasing drugs through cryptomarkets has tripled since 2014 reaching 15 percent of the 2020 respondents (GDS).
In this study, we assess social media data from active opioid users to understand what are the behaviors associated with opioid usage to identify what types of feelings are expressed. We employ deep learning models to perform sentiment and emotion analysis of social media data with the drug entities derived from cryptomarkets.
The team developed an AI to crawl three popular cryptomarkets where drugs are sold in order to determine nuanced information about what people were searching for and purchasing.
Then they crawled popular drug-related subreddits on Reddit such as r/opiates and r/drugnerds for posts related to the cryptomarket terminology in order to gather emotional sentiment. Where the researchers found difficulties in gathering enough Reddit posts with easy-to-label emotional sentiment, they found Twitter posts with relevant hashtags to fill in the gaps.
The end result was a data cornucopia that allowed the team to determine a robust emotional sentiment analysis for various substances.
In the future, the team hopes to find a way to gain better access to dark web cryptomarkets in order to create stronger sentiment models. The ultimate goal of the project is to help healthcare professionals better understand the relationship between mental health and substance abuse.
Per the team’s paper:
To identify the best strategies to reduce opioid misuse, a better understanding of cryptomarket drug sales that impact consumption and how it reflects social media discussions is needed.
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