Enabling Real-Time Drug Abuse Detection in Tweets

Phan, NhatHai; Chun, Soon Ae; Bhole, Manasi; Geller, James

doi:10.1109/icde.2017.221

Cited by 34 publications

(18 citation statements)

References 13 publications

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“…Our previous work [27] showed the potential of applying machine-learning models in a drug-abuse monitoring system to detect drug-abuse-related tweets. Several other approaches also utilized machine-learning methods in detecting and analyzing drugrelated posts on Twitter.…”

Section: Background and Related Workmentioning

confidence: 99%

An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

et al. 2019

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Abuse of prescription drugs and of illicit drugs has been declared a "national emergency" [1]. This crisis includes the misuse and abuse of cannabinoids, opioids, tranquilizers, stimulants, inhalants, and other types of psychoactive drugs, which statistical analysis documents as a rising trend in the United States. The most recent reports from the National Survey on Drug Use and Health (NSDUH) [2] estimate that 10.6% of the total population of people ages 12 years and older (i.e., about 28.6 million people) misused illicit drugs in 2016, which represents an increase of 0.5% since 2015 [3]. According to the Centers for Disease Control and Prevention (CDC), opioid drugs were involved in 42,249 known deaths in 2016 nationwide [4]. In addition, the number of heroin-involved deaths has been increasing sharply for 5 years, and surpassed the number of firearm homicides in 2015 [5]. In April 2017, the Department of Health and Human Services announced their "Opioid Strategy" to battle the country's drug-abuse crisis [1]. In the Opioid Strategy, one of the major aims is to strengthen public health data collection, to inform a timeliness

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Section: Background and Related Workmentioning

confidence: 99%

An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

et al. 2019

Self Cite

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“…Another approach presented a machine learning technique for monitoring social media to identify prescription drug abuse [39]. The authors manually wrote down 300 tweets indicating the abuse of illegal drugs.…”

Section: Literature Reviewmentioning

confidence: 99%

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

Silva

Souza

Cruz

et al. 2019

Sensors

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This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient’s medication intake and self-monitoring of blood pressure rates. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known “white-coat effect.” We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today’s homes to the system. The second is a preprocessing layer that filters the data generated from the patient’s behavior. The third is a reasoning layer that decides how to act based on the patient’s activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient’s schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient’s home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.

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“…As a significant proportion of the tweets are meant towards increasing awareness against drug-abuse, we need to filter out tweets that promote or self-report drug-abuse from the rest of the tweets. Taking a cue from the works related to the automatic identification of prescription drug-abuse tweets [16], [17], [41], [42], we investigated several supervised classification based approaches to filter out drug-abuse tweets from the set of tweets collected using keyword search. We proceeded with text classification as follows:…”

Section: A Data Collectionmentioning

confidence: 99%

A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets

Sequeira

Gayen

Ganguly

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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In this paper, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify drug abuse, to characterize the spreading of drug abuse tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that cascades of drug abuse tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected non-fringe nodes that work together towards such spread, thus potentially complicating the process of arresting such cascades. Further, we discovered that the engagement of the users with respect to certain drugs like Vicodin, Percocet and OxyContin, that were observed to be most mentioned in Twitter, is instantaneous. On the other hand for drugs like Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.

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Enabling Real-Time Drug Abuse Detection in Tweets

Cited by 34 publications

References 13 publications

An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets

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