JMIR Publications

ABSTRACT

Background:

In this paper we review four cases involving terms of use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized data scraping of social media data, entry of false information, misrepresentation of researcher identities to participants on forums, lack of IRB approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. Rather than regard these episodes solely as violations, this manuscript seeks to address ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. It is clear that the social media research landscape is evolving rapidly and that new ethical paradigms must be explored. Reliance on the current U.S. Department of Health and Human Services “Common Rule” is inadequate and due to the public availability of social media data there is often confusion between public and private spaces. In addition, the availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical consideration, e.g., computer and data scientists. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correction of inaccuracies in the literature (including retraction of publications and conference presentations). Most importantly, researchers need to assure participants that their research will not compromise anonymity or lead to harmful outcomes. Based on our experience and published recommendations by social media researchers, we offer potential prevention-oriented measures that can be applied by data producers, computer/data scientists, ethics committees, and publishers.

Objective:

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Methods:

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Results:

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Conclusions:

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