"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

Roy, Kaushik; Lokala, Usha; Khandelwal, Vedant; Sheth, Amit

doi:10.48550/arxiv.2102.01222

“…Further, we have identified processes for future research. We plan to expand this work to extract mental health symptoms from the drug-related social media data to connect the association between drugs and mental health problems, for example, the association between cannabis and depression [ 72 73 ]. We also plan to build an opioid drug social media knowledge graph (ODSM-KG) with all the diverse data points (drug, sentiment, emotion, mental health symptom, and location) and compare it against the state-of-the-art “Knowledge Graph based Approach For Exploring The U.S. Opioid Epidemic” [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study

Lokala,

Phukan,

Dastidar

et al. 2024

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Background Opioid and substance misuse has become a widespread problem in the United States, leading to the “opioid crisis.” The relationship between substance misuse and mental health has been extensively studied, with one possible relationship being that substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. Objectives This study aims to analyze social media posts related to substance use and opioids being sold through cryptomarket listings. The study aims to use state-of-the-art deep learning models to generate sentiment and emotion from social media posts to understand users’ perceptions of social media. The study also aims to investigate questions such as which synthetic opioids people are optimistic, neutral, or negative about; what kind of drugs induced fear and sorrow; what kind of drugs people love or are thankful about; which drugs people think negatively about; and which opioids cause little to no sentimental reaction. Methods The study used the drug abuse ontology and state-of-the-art deep learning models, including knowledge-aware Bidirectional Encoder Representations From Transformers–based models, to generate sentiment and emotion from social media posts related to substance use and opioids being sold through cryptomarket listings. The study crawled cryptomarket data and extracted posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. The study performed topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people’s responses to various drugs. Additionally, the study analyzed time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. Results The study found that the most effective model performed well (statistically significant, with a macro–F1-score of 82.12 and recall of 83.58) in identifying substance use disorder. The study also found that there were varying levels of sentiment and emotion associated with different synthetic opioids, with some drugs eliciting more positive or negative responses than others. The study identified topics that correlated with people’s responses to various drugs, such as pain relief, addiction, and withdrawal symptoms. Conclusions The study provides insight into users’ perceptions of synthetic opioids based on sentiment and emotion expressed in social media posts. The study’s findings can be used to inform interventions and policies aimed at reducing substance misuse and addressing the opioid crisis. The study demonstrates the potential of deep learning models for analyzing social media data to gain insights into public health issues.

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“…Some works have focused on the detection of specific psychological illnesses from posted tweets such as depression detection [40], [41], and detection of post-traumatic stress disorder [42]. Roy et al [43] investigated the effects of the cannabis drug on psychological health.…”

Section: Twitter Data In Psychological Health Researchmentioning

confidence: 99%

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Alswedani¹,

Mehmood²,

Katib³

et al. 2023

Preprint

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Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

show abstract

“…Some works have focused on the detection of specific psychological illnesses from posted tweets such as depression detection [41], [42], and detection of post-traumatic stress disorder [43]. Roy et al [44] investigated the effects of the cannabis drug on psychological health.…”

Section: Twitter Data In Psychological Health Researchmentioning

confidence: 99%

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Alswedani¹,

Mehmood²,

Katib³

et al. 2023

Preprint

View full text Add to dashboard Cite

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

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"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

Cited by 5 publications

References 43 publications

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

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