Conference item
The ethical challenges of publishing Twitter data for research dissemination
- Abstract:
- Empirical research involving the analysis of Internet-based data raises a number of ethical challenges. One instance of this is the analysis of Twitter data, in particular when specific tweets are reproduced for the purposes of dissemination. Although Twitter is an open platform it is possible to question whether this provides a sufficient ethical justification to collect, analyse and reproduce tweets for the purposes of research or whether it is necessary to also undertake specific informed consent procedures. This paper reports on an ethics consultation that formed part of a wider research study and that aimed to identify best practice procedures for the publication of Twitter data in research findings. We focus largely on the UK context and draw on the outcomes of the consultation to highlight the range and depth of ethical issues that arise in this area. We can see Twitter as a case study for a wide number of data sources used in Web Science. This is a highly complex landscape in which questions crystallise around fundamental principles such as informed consent, anonymisation and the minimisation of harm. Furthermore, tensions exist between commercial, regulatory and academic practices, and there are also circumstances in which good ethical practice might compromise academic integrity. There is an absence of consensus in Web science and related fields over how to resolve these issues and we argue that constructive debate is necessary in order to take a proactive approach towards good practice.
- Publication status:
- Published
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- Files:
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(Preview, Accepted manuscript, pdf, 2.0MB, Terms of use)
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- Publisher copy:
- 10.1145/3091478.3091489
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- ACM Web Science 2017, June 25-28, 2017, Troy, NY, USA
- Journal:
- WebSci ’17, June 25-28, 2017, Troy, NY, USA More from this journal
- Publication date:
- 2017-06-26
- Acceptance date:
- 2017-05-10
- DOI:
- Keywords:
- Pubs id:
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pubs:697046
- UUID:
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uuid:25b289d6-93ec-4f67-a8e3-1efc8b9f8edf
- Local pid:
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pubs:697046
- Source identifiers:
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697046
- Deposit date:
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2017-05-24
Terms of use
- Copyright holder:
- © 2017 Webb
- Copyright date:
- 2017
- Notes:
- This is the author accepted manuscript following peer review version of the article. The final version is available online from Association for Computing Machinery at: 10.1145/3091478.3091489
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