"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb

• URL: http://arxiv.org/abs/2304.10512v1
• Date: Thu, 20 Apr 2023 17:47:13 GMT
• Title: "Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb
• Authors: Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth
• Abstract summary: This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids.
• Score: 0.08388591755871731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions 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 are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically significant) with (macroF1=82.12, recall =83.58) to identify substance use disorder.

Related papers

• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network [11.491225833044021]
  We take advantage of recent advances in machine learning to identify opioid users on Reddit. posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. We apply an attention-based bidirectional long short memory model to identify opioid users.
  arXiv  Detail & Related papers  (2024-02-09T22:12:20Z)
• Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts [26.161892748901252]
  We present a corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use. For every post, we annotate span-level explanations and crucially study their role both in annotation quality and model development.
  arXiv  Detail & Related papers  (2023-11-15T16:05:55Z)
• MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks [49.60689355674541]
  A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
  arXiv  Detail & Related papers  (2023-10-30T15:57:32Z)
• The Evolution of Substance Use Coverage in the Philadelphia Inquirer [5.417001678982669]
  This study analyzes 157,476 articles published in the Philadelphia Inquirer over a decade. Cannabis and narcotics are the most frequently discussed classes of drugs. Hallucinogenic drugs are portrayed more positively than other categories, whereas narcotics are portrayed the most negatively.
  arXiv  Detail & Related papers  (2023-07-03T19:09:04Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Cross-Modality Neuroimage Synthesis: A Survey [71.27193056354741]
  Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. An alternative solution is to explore unsupervised or weakly supervised learning methods to synthesize the absent neuroimaging data.
  arXiv  Detail & Related papers  (2022-02-14T19:29:08Z)
• eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket [10.220809005199781]
  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. Our findings can help shape policy to help isolate opioid use cases where timely intervention may be required.
  arXiv  Detail & Related papers  (2021-03-29T16:58:26Z)
• Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions [0.0]
  We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. We found evidence of understudied abusive behaviors like chewing fentanyl patches and dissolving buprenorphine sublingually.
  arXiv  Detail & Related papers  (2021-02-22T18:14:48Z)
• Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery [62.9980747784214]
  Substance Use Disorders involve the misuse of alcohol, opioids, marijuana, and methamphetamine. 1 in 12 U.S. adults have or have had a substance use disorder. National Institute on Drug Abuse estimates that SUDs cost the U.S. $520 billion annually.
  arXiv  Detail & Related papers  (2020-06-23T18:30:20Z)
• Whose Tweets are Surveilled for the Police: An Audit of Social-Media Monitoring Tool via Log Files [69.02688684221265]
  We obtained log files from the Corvallis (Oregon) Police Department's use of social media monitoring software called DigitalStakeout. These log files include the results of proprietary searches by DigitalStakeout that were running over a period of 13 months and include 7240 social media posts. We observe differences in the demographics of the users whose Tweets are flagged by DigitalStakeout compared to the demographics of the Twitter users in the region.
  arXiv  Detail & Related papers  (2020-01-23T19:35:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.