Adverse Health Effects of Kratom: An Analysis of Social Media Data

Wahbeh, Abdullah; Nasralah, Tareq; El‐Gayar, Omar F.; Al-Ramahi, Mohammad; Elnoshokaty, Ahmed

doi:10.24251/hicss.2021.477

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…To infer topics, Latent Dirichlet Allocation (LDA)-based topic modeling is applied, as it has already been used in similar scenarios [12,14,27]. In LDA topic modeling, each document consists of a mixture of topics, T = {t1, t2,…, tm}, described by discrete probability distributions θd [36]. Each topic consists of a mixture of vocabulary words, W = {w1, w2,…, wk}, described by a discrete probability distribution βt [36].…”

Section: Topic Modelingmentioning

confidence: 99%

“…In LDA topic modeling, each document consists of a mixture of topics, T = {t1, t2,…, tm}, described by discrete probability distributions θd [36]. Each topic consists of a mixture of vocabulary words, W = {w1, w2,…, wk}, described by a discrete probability distribution βt [36]. The generative process can be described as follows [35][36][37]:…”

Section: Topic Modelingmentioning

confidence: 99%

“…Each topic consists of a mixture of vocabulary words, W = {w1, w2,…, wk}, described by a discrete probability distribution βt [36]. The generative process can be described as follows [35][36][37]:…”

Section: Topic Modelingmentioning

confidence: 99%

“…The LDA outputs are a topic-document matrix and a topic-term matrix. The size of the topic-document matrix is n × m, with the weight wi,j being the association of a document di and a topic tj [36]. The topic-word matrix has the size m × k, the weight wi,j is the association of the topic ti and a word wj [36].…”

Section: Topic Modelingmentioning

confidence: 99%

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Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs

Koss¹,

Bohnet-Joschko²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Pharmaceutical companies increasingly must consider patients' needs in drug development. Since patients' needs are often difficult to measure, especially in rare diseases, information in drug development decision-making is limited. In the proposed study, we employ the opportunity algorithm to identify and prioritize unmet medical needs of multiple sclerosis patients shared in social media posts. Using topic modeling and sentiment analysis features of the opportunity algorithm are generated. The result implies that sensory problems, pain, mental health problems, fatigue and sleep disturbances represent the highest unmet medical needs of the samples population. The present study suggests a promising potential of this method to provide relevant insights into rare disease populations to promote patient-centered drug development.

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Section: Topic Modelingmentioning

confidence: 99%

Section: Topic Modelingmentioning

confidence: 99%

Section: Topic Modelingmentioning

confidence: 99%

Section: Topic Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs

Koss¹,

Bohnet-Joschko²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Popular social media platforms, including Twitter, have enabled new channels for users to share information and their experiences [12]. These platforms provide efficient methods of information access for health surveillance and social intelligence [13][14][15], and they have a growing popularity for sharing and debating scientific information [16][17][18]. Several studies have used Twitter as a data source to demonstrate the potential to identify the public's reactions to a variety of public health concerns, including the opioid crisis [19], marijuana [20][21][22], and vaping [23].…”

Section: Introductionmentioning

confidence: 99%

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Al-Ramahi¹,

Elnoshokaty²,

El‐Gayar³

et al. 2021

JMIR Public Health Surveill

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Background Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

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Associations of Lifetime Nonmedical Opioid, Methamphetamine, and Kratom Use within a Nationally Representative US Sample

Smith

Rogers

Strickland

2021

Journal of Psychoactive Drugs

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Adverse Health Effects of Kratom: An Analysis of Social Media Data

Cited by 6 publications

References 14 publications

Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs

Social Media Mining in Drug Development Decision Making: Prioritizing Multiple Sclerosis Patients’ Unmet Medical Needs

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Associations of Lifetime Nonmedical Opioid, Methamphetamine, and Kratom Use within a Nationally Representative US Sample

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