A Proposed Approach for Conducting Studies That Use Data From Social Media Platforms

D’Souza, Ryan S; Hooten, W. Michael; Murad, M. Hassan

doi:10.1016/j.mayocp.2021.02.010

Cited by 22 publications

(15 citation statements)

References 73 publications

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“…21,26 Only a few studies directly mentioned existing frameworks and standards such as EMA recommendations, 27 FAIR principles, 28 openHR 21 or DAQORD framework 19 in this context. Recent efforts to provide guidelines are mainly focused either on a specific type of unstructured data or specific challenges, for example, guidelines and standards for the use of social media data, 56,57 guidelines regarding the use of EHRs, 58,59 checklists and frameworks for evaluating the measurements made by digital technologies 60 or algorithms used for data analysis. 61,62 However, it is up for discussion whether these particular frameworks and guidelines are suitable to provide general guidance on challenges connected with digital unstructured data enrichment.…”

Section: Discussionmentioning

confidence: 99%

Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review

Sedláková

Daniore

Wintsch

et al. 2022

Preprint

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Digital data play an increasingly important role in advancing medical research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Specifically, unstructured data are available in a non-standardized format and require substantial preprocessing and feature extraction to translate them to meaningful insights. This might hinder their potential to advance health research, prevention, and patient care delivery, as these processes are resource intensive and connected with unresolved challenges. These challenges might prevent enrichment of structured evidence bases with relevant unstructured data, which we refer to as digital unstructured data enrichment. While prevalent challenges associated with unstructured data in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with existing data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health along with possible solutions to address these challenges. Building on these findings, we compiled a checklist following the standard data flow in a research study to contribute to the limited available systematic guidance on digital unstructured data enrichment. This proposed checklist offers support in early planning and feasibility assessments for health research combining unstructured data with existing data sources. Finally, the sparsity and heterogeneity of unstructured data enrichment methods in our review call for a more systematic reporting of such methods to achieve greater reproducibility.

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Section: Discussionmentioning

confidence: 99%

Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review

Sedláková

Daniore

Wintsch

et al. 2022

Preprint

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“…In our study, we made a useful attempt towards this goal. In future work, we aim to enrich the contents of the classifiers by testing different sets of language features such as sentiment features [34,35]. We also aim to test our methods with mental health resources in languages other than English to help better support mental health organizations working with multicultural, multilingual populations.…”

Section: Discussionmentioning

confidence: 99%

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

Xie

Huang

et al. 2021

IJERPH

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We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information.

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“…Here, we offer guidance for healthcare professionals for interpreting studies that use social media information. Guidance on how to conduct these studies has been previously published 8…”

Section: Introductionmentioning

confidence: 99%

“…For instance, depending on the study’s main objectives, using a ‘view count’ filter may be useful to identify the content most disseminated and subsequently most likely to have impacted the perspective of viewers; using a ‘relevance’ filter may identify content that closely matches search terms and using a ‘date of creation’ filter may identify most recent and updated content. Use of filters to specify platform queries has been previously described 8…”

Section: Introductionmentioning

confidence: 99%

Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms

D’Souza

Daraz

Hooten

et al. 2021

BMJ EBM

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A Proposed Approach for Conducting Studies That Use Data From Social Media Platforms

Cited by 22 publications

References 73 publications

Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review

Challenges and best practices for digital unstructured data enrichment in health research: a systematic narrative review

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

Users' Guides to the Medical Literature series on social media (part 2): how to appraise studies using data from platforms

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