Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets

Han, Hu; Phan, Nhat Hai; Geller, James; Vo, Huy T.; Bhole, Manasi; Huang, Xueqi; Lorio, Sophie Di; Dinh, Thang N.; Chun, Soon Ae

doi:10.1007/978-3-030-04648-4_28

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…In particular, sentiment is a commonly used metric to investigate the positive or negative reports contained in these messages. For example, authors in [8] analyzed a tweet of risky behavior related to drug abuse as a positive tweet, or a tweet of safe behavior as a negative tweet. A rich variety of techniques have been proposed in this context ranging from dictionary-based methods using annotated knowledge, supervised machine learning methods to fully unsupervised methods, in conjunction with statistical methods such as affinity analysis and cluster occurrences of words, thereby inducing word sentiment intensities.…”

Section: Bio-medical Sentiment Analysismentioning

confidence: 99%

Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

Grissette

Nfaoui

2021

Pers Ubiquit Comput

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Owing to the quick spread of minute health-related experiences, the distillation of knowledge from such unstructured narratives is an extremely challenging task. In spite of the success of neural networks methods in improving learning structural reliability, they result in inadequate accuracy of the bio-medical sentiment classification when employing less useful features sets. Therefore, they lack discriminatory potential. In this study, we propose to add a deep associative memory into neural networks for an effective sentiment decomposition, which emphasizes correctly on bio-medical entities related to the extraction of different data-object properties, and contextual-semantics dependencies for a given aspect. The underlying trust of these measures is behind the ability to compute the completion of unseen medical patterns, where comprehensive bio-medical distributed representations are used for representing the formal medical connections from PubMed databases. Experiments on a biomedical sentiment analysis task show that the model provides comprehensive embeddings with meaningful medical patterns. It achieved an average performance of 87% on varied large online datasets. It also outperforms baselines in discovering and identifying medical natural concepts. We provide meaningful support to bio-medical sentiment analysis applications in social networks. Indeed, the facets of this study might be used in many health concerns such as analyzing change in health status or unexpected situations.

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Section: Bio-medical Sentiment Analysismentioning

confidence: 99%

Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

Grissette

Nfaoui

2021

Pers Ubiquit Comput

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“…For the data collection, we use tweets as a major source of geotagged social media data for its availability and abundance. We collect tweets through the publicly available Streaming API [19] using a typical keyword-based crawler well-integrated with trained deep learning models (i.e., CNN and LSTM models) designed to detect drug abuse risk behaviors in tweets [7,8]. In our previous works [7,8], we built our human labeled drug abuse risk behavior dataset, and demonstrated that a deep learning model, which was trained with both labeled data and large number of unlabeled data, can achieve state-of-art classification performance (86.63% of Accuracy, 89% of Recall, 86.83% of F1-value) on our dataset.…”

Section: Back-end Servicesmentioning

confidence: 99%

“…We collect tweets through the publicly available Streaming API [19] using a typical keyword-based crawler well-integrated with trained deep learning models (i.e., CNN and LSTM models) designed to detect drug abuse risk behaviors in tweets [7,8]. In our previous works [7,8], we built our human labeled drug abuse risk behavior dataset, and demonstrated that a deep learning model, which was trained with both labeled data and large number of unlabeled data, can achieve state-of-art classification performance (86.63% of Accuracy, 89% of Recall, 86.83% of F1-value) on our dataset. The module is able to continuously collect newest tweets, to feed tweets to deep learning models, and to update the system with live data, so that the drug trend can be tracked and analyzed in near real-time.…”

Section: Back-end Servicesmentioning

confidence: 99%

DrugTracker

Han

Phan

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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In this paper, we present a community-focused drug abuse monitoring and supporting system, called DrugTracker, that utilizes social media and geospatial data in near real-time. Through the system, users can: (1) Detect drug abuse risk behaviors from social media platforms, e.g., Twitter; (2) Analyze drug abuse risk behaviors by querying consolidated and live datasets with keywords, spatial entities, and time constraints; and (3) Explore the query results and associated data through a web-based user interface in thematic choropleth, heatmap, and statistical charts. To protect the privacy of the Twitter users, whose data is collected, the system automatically hides the re-identification elements in tweets and aggregates the geo-tags into areas such as census tracts. For the demonstration purpose, our DrugTracker system is populated with a database that contains about 10 million tweets from the year 2017, that were annotated as drug abuse risk behavior positive by our deep learning model. CCS CONCEPTS• Information Systems → Data Mining; • Collaborative and social computing → Social networks.

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“…More recently, researchers have examined patterns of anonymity in web-based recovery communities [13]. Specific to OUD, previous studies have investigated the different types of web-based discourse associated with opioid use, including personal use, whether it is associated with legitimate use or abuse of opioids [14], or whether it involves the promotion of clinically unverified treatments [15]. Abuse discourse on social media platforms has been further broken down into stand-alone use and co-use of opioids with other opioids, illicit drugs, and alcohol [16].…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

ElSherief¹,

Sumner²,

Jones³

et al. 2021

J Med Internet Res

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Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

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Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets

Cited by 14 publications

References 20 publications

Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

DrugTracker

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

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