Ethical Challenges in Studying Sensitive Online Communities

The next presenter at the 2019 AoIR Flashpoint Symposium is Ylva Hård af Segerstad, who begins by pointing out how much harder the study of social media phenomena has become as platform APIs have been curtailed and closed down. Additionally, and relatedly, new policy settings such as the European GDPR, have also imposed new limits on data collection, processing, and sharing. This creates critical new ethical challenges for research.

Ylva’s own work focusses on online communities supporting bereaved parents; such communities are especially important in societies such as Sweden, where cultural values mean that death and other ‘difficult’ topics are generally not discussed and prolonged public grieving is considered inappropriate. Social media provide opportunities for such communities to provide help to each other, in closed online communities.

Such communities provide digital safe-havens, sometimes also supported by offline support groups. The Swedish support group Ylva studied has been active since 2010 and generates a substantial amount of posts per day; it also serves as an important space for bereaved parents to maintain their bonds with their deceased children. Within this group, these parents say, they don’t feel judged for their grief.

But studying such a vulnerable population highlights a complex set of ethical challenges, and highlights the fact that research ethics is an ongoing process throughout the study, from design through data collection and analysis to the dissemination of results. Methods and ethics cannot be separated here, but are explicitly intertwined. Indeed, there is an ethical responsibility beyond the ethical guidelines that may have been imposed by universities and funding bodies.

Ethical approaches must be flexible and context-sensitive here, and this is especially critical where the research involves mixed-methods and qualitative approaches such as autoethnographies, participant observations, interviews, and media go-alongs in addition to large-scale, aggregate, quantitative analysis.