Assessment of Antipsychotic Medications on Social Media: Machine Learning Study

Álvarez-Mon, Miguel Ángel; Donat‐Vargas, Carolina; Santoma-Vilaclara, Javier; Anta, Laura de; Goena, Javier; Sánchez-Bayona, Rodrigo; Mora, Fernando A. Lázaro; Ortega, Miguel A.; Lahera, Guillermo; Rodríguez‐Jiménez, Roberto; Quintero, Javier; Álvarez‐Mon, Melchor

doi:10.3389/fpsyt.2021.737684

Cited by 12 publications

(11 citation statements)

References 67 publications

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“…All remaining tweets were considered for thematic content analysis. Secondly, a codebook was created based on our research questions, our previous experience in analyzing tweets, and what we determined to be the most common tweet themes ( 28 – 31 ). Third, two raters separately analyzed 100 tweets to test the suitability of the codebook.…”

Section: Methodsmentioning

confidence: 99%

Analyzing Psychotherapy on Twitter: An 11-Year Analysis of Tweets From Major U.S. Media Outlets

et al. 2022

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BackgroundThe Internet has become the main source of information on health issues, and information now determines the therapeutic preferences of patients. For this reason, it is relevant to analyze online information discussing psychotherapy.ObjectiveTo investigate tweets posted by 25 major US media outlets between 2009 and 2019 concerning psychotherapy.MethodsWe investigated tweets posted by 25 major US media outlets about psychotherapy between January 2009 and December 2019 as well as the likes generated. In addition, we measured the sentiment analysis of these tweets.ResultsMost of the tweets analyzed focused on Mindfulness (5,498), while a low number were related to Psychoanalysis (376) and even less to Cognitive-Behavioral Therapy (61). Surprisingly, Computer-supported therapy, Psychodynamic therapy, Systemic therapy, Acceptance and commitment therapy, and Dialectical behavior therapy did not generate any tweet. In terms of content, efficacy was the main focus of the posted tweets, receiving Cognitive-Behavioral Therapy and Mindfulness a positive appraisal.ConclusionsUS media outlets focused their interest on Mindfulness which may have contributed to the growing popularity in the past years of this therapeutic modality.

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Section: Methodsmentioning

confidence: 99%

Analyzing Psychotherapy on Twitter: An 11-Year Analysis of Tweets From Major U.S. Media Outlets

et al. 2022

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“…When discussing this study in the context of previous research on mental health topics on Twitter, it is noted that the total amount of retrieved publications for ECT is lower than the amount of retrieved tweets in other studies related to other treatments for mental disorders including antipsychotics, antidepressants, and medications for attention-deficit/hyperactivity disorder [27, 28, 34]. They are even lower in amount than tweets for nonpsychiatric disorders, such as statins, medications for obesity, chemotherapy agents, or antibiotics [24, 34–36].…”

Section: Discussionmentioning

confidence: 99%

“…The aforementioned previous Twitter research related to psychopharmacological drugs also described the types of users involved in discussions related to such treatments [ 28 , 34 ]. For antidepressants and antipsychotics, people with lived experience and their relatives were authors of more than half the tweets, which contrasts with the lower relative proportions of tweets from such users in ECT tweets in our study.…”

Section: Discussionmentioning

confidence: 99%

“…Another study in the same year compared trivialization between mental disorders and physical health conditions, demonstrating that such contents were 2.10 higher in the former [ 47 ]. Our research team has analyzed in the past stigmatizing attitudes regarding psychopharmacological treatments finding notable proportions of such contents, which resemble proportions found for discussions on mental disorders [ 28 , 34 ]. This leads us to think that stigmatization of mental disorders extends to psychopharmacological treatments [ 50 ], and it is remarkable that such stigma seems much higher for ECT, being present in more than half of their tweets of our current sample.…”

Section: Discussionmentioning

confidence: 99%

“…This novel approach is also able to include those individuals reluctant to fill out questionnaires and surveys [24]. Moreover, as social media discussions happen in settings more casual and spontaneous than healthcare appointments and formal surveys, they are more likely to reflect sincere views and beliefs [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of beliefs and attitudes about electroconvulsive therapy posted on Twitter: An observational study

et al. 2023

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Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach

Nishiyama¹,

Yada²,

Wakamiya³

et al. 2023

J Med Internet Res

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Background Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media–based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. Objective This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. Methods This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). Results The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. Conclusions The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured.

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Assessment of Antipsychotic Medications on Social Media: Machine Learning Study

Cited by 12 publications

References 67 publications

Analyzing Psychotherapy on Twitter: An 11-Year Analysis of Tweets From Major U.S. Media Outlets

Analyzing Psychotherapy on Twitter: An 11-Year Analysis of Tweets From Major U.S. Media Outlets

Assessment of beliefs and attitudes about electroconvulsive therapy posted on Twitter: An observational study

Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach

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