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Accepted for/Published in: Online Journal of Public Health Informatics

Date Submitted:

Open Peer Review Period: -

Date Accepted:

Date Submitted to PubMed:

closed for review but you can still tweet
  • Alhasan Ahmed A, Qingyu W
  • Machine Learning–Based Detection of Adverse Drug Reaction–Related Posts From Chinese Social Media: Comparative Machine Learning Study
  • Online Journal of Public Health Informatics
  • DOI: 10.2196/11848
  • PMID: 30303485
  • PMCID: 6352016

Abstract accepted

Background:

Social media can be a useful strategy for recruiting hard-to-reach, stigmatized populations into research studies; however, it may also introduce risks for participant and research team exposure to negative comments. Currently, there is no published formal social media recruitment and monitoring guidelines that specifically address harm reduction for social media recruitment of marginalized populations.

Objective:

Social media can be a useful strategy for recruiting hard-to-reach, stigmatized populations into research studies; however, it may also introduce risks for participant and research team exposure to negative comments. Currently, there is no published formal social media recruitment and monitoring guidelines that specifically address harm reduction for social media recruitment of marginalized populations.

Methods:

Social media can be a useful strategy for recruiting hard-to-reach, stigmatized populations into research studies; however, it may also introduce risks for participant and research team exposure to negative comments. Currently, there is no published formal social media recruitment and monitoring guidelines that specifically address harm reduction for social media recruitment of marginalized populations.

As per the author’s request the PDF is not available.