Social media mining in drug development—Fundamentals and use cases

Koss, Jonathan; Rheinlaender, Astrid; Truebel, Hubert; Bohnet-Joschko, Sabine

doi:10.1016/j.drudis.2021.08.012

Cited by 11 publications

(10 citation statements)

References 72 publications

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“…Another possibility could be to capture correlations between substances and effects mentioned in a sentence by the application of dependency parsing [ 68 ] or by implementing an observational study design [ 69 ]. However, reliable assessment of causality of underlying treatment effects is impossible because of various limitations [ 19 ]; for instance, inaccurate use of medical terminology by users would bias the results, even if machine-learning algorithms perform with perfect accuracy. Moreover, our analysis indicates a trend of polymedication, which would confound the analysis of single substances.…”

Section: Discussionmentioning

confidence: 99%

“…For example, hyperlinks, tabs, and blank lines were removed. The substances of interest mentioned by patients in posts needed to be extracted and structured for subsequent analysis [ 19 ] using NER [ 28 ]. We defined substances of interest as explicitly mentioned substances or groups of substances that can be considered as treatments.…”

Section: Methodsmentioning

confidence: 99%

“…While traditional crowdsourcing aims at active collaboration (eg, between a pharmaceutical company and an external patient group), online forums enable a passive approach to collecting real-world data by offering relevant content for analysis, which is also called “passive crowdsourcing” [ 17 , 18 ]. For instance, researchers have analyzed data from disease-specific social media platforms to identify medications that are used outside the approved indication (off-label use) [ 10 , 19 ]. Off-label use provides information to support hypotheses regarding DR [ 10 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, researchers have analyzed data from disease-specific social media platforms to identify medications that are used outside the approved indication (off-label use) [ 10 , 19 ]. Off-label use provides information to support hypotheses regarding DR [ 10 , 19 ]. These approaches save time and costs associated with data collection, while incorporating patients’ real-world experiences.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches save time and costs associated with data collection, while incorporating patients’ real-world experiences. However, social media mining (SMM) [ 19 ], a term that refers to the collection of methods used for conducting passive crowdsourcing, poses significant risks in terms of bias [ 10 , 19 ]. For instance, owing to the age-related user behavior on social media platforms, the data might not be generalizable to the whole population [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing

Koss¹,

Bohnet-Joschko²

2022

JMIR Form Res

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Background Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called “long-COVID.” Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. Objective The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users’ self-reports to support hypothesis generation for drug repurposing, by incorporating patients’ experiences. Methods We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the “/r/covidlonghaulers” subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. Results The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. “Histamine antagonists,” “famotidine,” “magnesium,” “vitamins,” and “steroids” were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. Conclusions This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%