Limits...
Ethical issues in using Twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature.

Conway M - J. Med. Internet Res. (2014)

Bottom Line: We then read the full text of these 49 articles and discarded 36, resulting in a final inclusion set of 13 articles.Ethical concepts were then identified in each of these 13 articles.From these 13 articles, we iteratively generated a taxonomy of ethical concepts consisting of 10 top level categories: privacy, informed consent, ethical theory, institutional review board (IRB)/regulation, traditional research vs Twitter research, geographical information, researcher lurking, economic value of personal information, medical exceptionalism, and benefit of identifying socially harmful medical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California San Diego, Department of Family and Preventive Medicine, La Jolla, CA, United States. mike.conway@utah.edu.

ABSTRACT

Background: The rise of social media and microblogging platforms in recent years, in conjunction with the development of techniques for the processing and analysis of "big data", has provided significant opportunities for public health surveillance using user-generated content. However, relatively little attention has been focused on developing ethically appropriate approaches to working with these new data sources.

Objective: Based on a review of the literature, this study seeks to develop a taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data, with a view to: (1) explicitly identifying a set of potential ethical issues and concerns that may arise when researchers work with Twitter data, and (2) providing a starting point for the formation of a set of best practices for public health surveillance through the development of an empirically derived taxonomy of ethical concepts.

Methods: We searched Medline, Compendex, PsycINFO, and the Philosopher's Index using a set of keywords selected to identify Twitter-related research papers that reference ethical concepts. Our initial set of queries identified 342 references across the four bibliographic databases. We screened titles and abstracts of these references using our inclusion/exclusion criteria, eliminating duplicates and unavailable papers, until 49 references remained. We then read the full text of these 49 articles and discarded 36, resulting in a final inclusion set of 13 articles. Ethical concepts were then identified in each of these 13 articles. Finally, based on a close reading of the text, a taxonomy of ethical concepts was constructed based on ethical concepts discovered in the papers.

Results: From these 13 articles, we iteratively generated a taxonomy of ethical concepts consisting of 10 top level categories: privacy, informed consent, ethical theory, institutional review board (IRB)/regulation, traditional research vs Twitter research, geographical information, researcher lurking, economic value of personal information, medical exceptionalism, and benefit of identifying socially harmful medical conditions.

Conclusions: In summary, based on a review of the literature, we present a provisional taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data.

Show MeSH
Inclusion/exclusion flowchart.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4285736&req=5

figure2: Inclusion/exclusion flowchart.

Mentions: Our initial set of queries identified 342 references across the four databases (see Figure 2). After title and abstract screening, 49 references remained. After further full-text screening of these 49 references, 13 remained. Five of the papers were from biomedical journals [13,16-19] and six were from computer science and engineering conference proceedings [20-25]. One paper appeared in a journal dedicated to the social and cultural impact of technology [14]. Finally, one paper was published in the proceedings of a collaborative technology conference [26]. All articles were peer reviewed and written in English.


Ethical issues in using Twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature.

Conway M - J. Med. Internet Res. (2014)

Inclusion/exclusion flowchart.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4285736&req=5

figure2: Inclusion/exclusion flowchart.
Mentions: Our initial set of queries identified 342 references across the four databases (see Figure 2). After title and abstract screening, 49 references remained. After further full-text screening of these 49 references, 13 remained. Five of the papers were from biomedical journals [13,16-19] and six were from computer science and engineering conference proceedings [20-25]. One paper appeared in a journal dedicated to the social and cultural impact of technology [14]. Finally, one paper was published in the proceedings of a collaborative technology conference [26]. All articles were peer reviewed and written in English.

Bottom Line: We then read the full text of these 49 articles and discarded 36, resulting in a final inclusion set of 13 articles.Ethical concepts were then identified in each of these 13 articles.From these 13 articles, we iteratively generated a taxonomy of ethical concepts consisting of 10 top level categories: privacy, informed consent, ethical theory, institutional review board (IRB)/regulation, traditional research vs Twitter research, geographical information, researcher lurking, economic value of personal information, medical exceptionalism, and benefit of identifying socially harmful medical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of California San Diego, Department of Family and Preventive Medicine, La Jolla, CA, United States. mike.conway@utah.edu.

ABSTRACT

Background: The rise of social media and microblogging platforms in recent years, in conjunction with the development of techniques for the processing and analysis of "big data", has provided significant opportunities for public health surveillance using user-generated content. However, relatively little attention has been focused on developing ethically appropriate approaches to working with these new data sources.

Objective: Based on a review of the literature, this study seeks to develop a taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data, with a view to: (1) explicitly identifying a set of potential ethical issues and concerns that may arise when researchers work with Twitter data, and (2) providing a starting point for the formation of a set of best practices for public health surveillance through the development of an empirically derived taxonomy of ethical concepts.

Methods: We searched Medline, Compendex, PsycINFO, and the Philosopher's Index using a set of keywords selected to identify Twitter-related research papers that reference ethical concepts. Our initial set of queries identified 342 references across the four bibliographic databases. We screened titles and abstracts of these references using our inclusion/exclusion criteria, eliminating duplicates and unavailable papers, until 49 references remained. We then read the full text of these 49 articles and discarded 36, resulting in a final inclusion set of 13 articles. Ethical concepts were then identified in each of these 13 articles. Finally, based on a close reading of the text, a taxonomy of ethical concepts was constructed based on ethical concepts discovered in the papers.

Results: From these 13 articles, we iteratively generated a taxonomy of ethical concepts consisting of 10 top level categories: privacy, informed consent, ethical theory, institutional review board (IRB)/regulation, traditional research vs Twitter research, geographical information, researcher lurking, economic value of personal information, medical exceptionalism, and benefit of identifying socially harmful medical conditions.

Conclusions: In summary, based on a review of the literature, we present a provisional taxonomy of public health surveillance-related ethical concepts that emerge when using Twitter data.

Show MeSH