Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study

Kim, Myeong Gyu; Kim, Jungu; Kim, Su Cheol; Jeong, Jaegwon

doi:10.2196/16466

Cited by 33 publications

(31 citation statements)

References 26 publications

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“…The analysis of these tweets reveals relevant information for health care providers because many patients that question the efficacy of treatments or abandon their treatments entirely tend to withhold this information from doctors due to feelings of shame or guilt [ 33 ]. Indeed, social media has been found to identify side effects not always uncovered via traditional surveys [ 44 ]. In semaglutide- and liraglutide-related tweets, most links were scientific, whereas with the rest of the drugs, the majority of links contained a nonscientific source.…”

Section: Discussionmentioning

confidence: 99%

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Álvarez-Mon¹,

Llavero-Valero²,

Barco³

et al. 2021

J Med Internet Res

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Background Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research. Objective The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs. Methods In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured. Results We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community. Conclusions This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users.

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Section: Discussionmentioning

confidence: 99%

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Álvarez-Mon¹,

Llavero-Valero²,

Barco³

et al. 2021

J Med Internet Res

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“…In contrast, we look at both Twitter and Reddit data to investigate how document classification models perform when tested in-and out-of data source to see how data source and size of context affect model performance. Kim at al. (2020) presented experiments on binary classification of tweets mentioning methylphenidate or related brand names as either non-medical use or side effects using a Support Vector Machine (SVM) as their underlying machine learning algorithm [12].…”

Section: Social Media Analysis For Healthcarementioning

confidence: 99%

“…Kim at al. (2020) presented experiments on binary classification of tweets mentioning methylphenidate or related brand names as either non-medical use or side effects using a Support Vector Machine (SVM) as their underlying machine learning algorithm [12]. Their best model, which was trained using a combination of training labels, features extracted from the tweet text as well as sentiment derived from each tweet, achieves high precision (>0.92) but fairly low recall.…”

Section: Social Media Analysis For Healthcarementioning

confidence: 99%

Classifying Patient and Professional Voice in Social Media Health Posts

Alex

Whyte

Duma

et al. 2021

Preprint

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Background: Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of socialmedia data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a Convolutional Neural Network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results: We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreedroughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.Conclusion: The main conclusion resulting from this work is that using more data for training a classifier does not necessarily result in best possible performance. In the context of classifying social media posts by patient and professional voice, we showed that it is best to train separate models per data source (Reddit andTwitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

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“…ADHD is one of the most common neurodevelopmental disorders of childhood characterized by inattention and/or hyperactive-impulsive behaviors [2][3][4]. Most recently, researchers have also been quite concerned about the increased incidence of MPH misuse among individuals not meeting the criteria for ADHD, as a "cognitive enhancer" and as an alternative to other psychostimulants for recreational use [5][6][7], as well as in preschool children with 2-4 years of age [8,9]. The MPH neuropharmacological pro le is similar to amphetamine and cocaine [10], but the complete mechanisms of MPH are still unknown [11,12].…”

Section: Introductionmentioning

confidence: 99%

Evidence that methylphenidate Treatment Evokes Anxiety-Like Behavior Through Glucose Hypometabolism and Disruption of the Orbitofrontal Cortex Metabolic Networks

Schmitz

Silveira

Venturin

et al. 2021

Preprint

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Methylphenidate (MPH) has been widely misused by children and adolescents who do not meet all diagnostic criteria for attention-deficit/hyperactivity disorder without a consensus about the consequences. Here, we evaluate the effect of MPH treatment on glucose metabolism and metabolic network in the rat brain, as well as on performance in behavioral tests. Wistar male rats received intraperitoneal injections of MPH (2.0 mg/kg) or an equivalent volume of 0.9% saline solution (controls), once a day, from the 15th to the 44th day of age. Fluorodeoxyglucose-18 was used to investigate the cerebral metabolism, and a cross-correlation matrix was used to examine the brain metabolic network in MPH-treated rats using micro-positron emission tomography imaging. Performance in the light-dark transition box, eating-related depression, and sucrose preference tests were also evaluated. While MPH provoked glucose hypermetabolism in the auditory, parietal, retrosplenial, somatosensory, and visual cortices; hypometabolism was identified in the left orbitofrontal cortex. MPH-treated rats show a brain metabolic network more efficient and connected, but careful analyzes reveal that the MPH interrupts the connection of the orbitofrontal cortex with other brain areas. Anxiety-like behavior was also observed in MPH-treated rats. This study shows that glucose metabolism evaluated by microPET in the brain can be affected by MPH in different ways according to the region of the brain studied. It may be related, at least in part, to a rewiring in brain the metabolic network and behavioral changes observed, representing an important step in exploring the mechanisms and consequences of MPH treatment.

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Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study

Cited by 33 publications

References 26 publications

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Classifying Patient and Professional Voice in Social Media Health Posts

Evidence that methylphenidate Treatment Evokes Anxiety-Like Behavior Through Glucose Hypometabolism and Disruption of the Orbitofrontal Cortex Metabolic Networks

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