There has been a very upset/angry reaction to a paper published using tweets about mental health. I'm not RTing because I'd like to talk about this without drawing more attention to the researchers or the community. But it's an important research ethics cautionary tale. [Thread]
The paper is a qualitative analysis of tweets about mental health care. It includes paraphrased quoted tweets that the researchers ensured were not searchable. The study was approved by an ethics review committee in the UK, and the paper cites the AOIR ethics guidelines.
The paper includes an ethics and consent section that includes the above and notes that because tweets are public, consent was not required. The study also included a researcher with mental health lived experience. There do not appear to be any other statements regarding ethics.
The fact that the research was reviewed by an ethics committee, that there was some consideration for researcher positionality, and that they employed ethical fabrication are steps beyond a lot of research like this.
However, the reaction shows that there was still actual harm.
The lead researcher (a student) tweeted a link to the paper, and replies suggest that despite the paraphrasing, multiple people recognized themselves and others in the tweets--including tweets from people who have since died.
I'm going to describe some harms expressed in replies and quote-tweets from people who identify as part of the community studied:
- feeling violated and exploited with respect to their content being used without consent, without knowledge, and for researchers' personal gain
- feels especially invasive with respect to this being a support resource, and that some of the people studied have died
- by studying content without talking to people, it does not properly include the community's voices or provide the opportunity for clarification
- the attempt at anonymization was insufficient since people were able to recognize themselves and others in the data
- the content analyzed is explicitly people talking about some of their worst experiences, and those experiences should not be exploited for research
- this community, and patients in general, are a vulnerable population that should be treated with special care by researchers
- in support communities on twitter people use pseudonyms to protect their privacy and amplifying their content might counteract those measures
So what should the researchers have done? Some pointed out that asking permission to use tweets quoted in the paper would have mitigated some harms. This is a strategy that I've seen before in mental health related research - not consent for analysis, but just for quoted content.
This example highlights something that is really important to remember--not ALL potential research harms are privacy-related. A finding in some of my work about public reactions to research is that some people fundamentally object to being studied without consent. That is a harm.
The fact that there was some ethical consideration from the researchers here (i.e. paraphrasing), suggesting that they had good intentions, also highlights how the norms of academia haven't caught up with the expectations of research subjects.
It's very important to remember there are no checklists here. Not "is it public," not IRB approval, not anonymization. Every research project & population studied has specific context. This was a vulnerable community that required a lot of consideration for potential harms.
For further reading, this is a summary of some of the work from myself and colleagues on this topic, including @moduloone's and my paper about how Twitter users feel about being studied. howwegettonext.com/scientists-lik…
To add to this, I want to point to another paper we published a couple of years ago, for two reasons: (1) One finding is that some people fundamentally object to being studied without their consent. (2) We analyzed publicly available content in this study. cmci.colorado.edu/~cafi5706/Unex…
The context was the Facebook emotional contagion study; we wanted to understand public reactions to (and harms of) a research ethics controversy, while avoiding the obvious selection bias. (People who hate research are unlikely to participate in research.)
We analyzed public comments on news articles. And in doing so we considered possible expectations of privacy (or not), the intent/context of this specific content, and weighed potential risks - and wrote about this in the paper.
But the point here is, some people might find this method to still be problematic, and others will think my explanation overkill. Even I find this tricky and I sometimes doubt myself. I have different practices and standards now for my own research than I did five years ago.
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The problem with workload (and thus work-life balance) in academia is:
You can always do more.
(A thread based on a recent personal epiphany.)
Unlike many other kinds of jobs, when you are a (research-heavy) professor no one tells you exactly what you need to do. Or even how much you need to do. There are things that are wonderful about this kind of freedom. But also, it means that you can always be doing more.
How many research projects should you be doing at any given time? How many papers should you be writing? You might have a personal sense for this, maybe even a rule-of-thumb, maybe even a mentor giving you advice. But whatever N is, it COULD always be N+1.
I'm often struck by how much the foundation of science relies on individual integrity. And typically I feel it's pretty solid. But this kind of garbage is a result of publish-or-perish, bean counting, and the general incentive structures of academia. cacm.acm.org/magazines/2021…
Also when this situation came to light a year ago I went on a whole lengthy tweet-rant about it so I won't repeat myself but here you go. :)
Also to clarify: "is a result of" above should probably be "exacerbated by" because obviously it's a result of when there is a breakdown of individual integrity. Awful people gonna be awful, but there are incentive structures dictating the particular form of awful.
Unsurprisingly, Reddit as a site of study or data source is on the rise. The first 2 papers we encountered were published in 2010, with a jump to 17 in 2013, and 230 in 2019.
I also think it's really interesting that though computing and related disciplines make up the largest number of journals represented in our dataset of Reddit papers, medicine and health is next - even (just) above social science.
I'm increasingly frustrated with "I don't know enough about ethics to include it in my class."
My take: If you e.g. teach an ML class & you actually don't know ANYTHING about ML ethics... learn. Ethics is part of ML. I'm sorry your education was lacking, but now time to learn.
Like, professors should be constantly continuing to learn. I learn new things so that I can teach them all the time. If you say "I don't know enough about ethics" what you probably mean is "I don't care enough about ethics."
I'm not suggesting that if you teach an ML class you need to go read Kant. I'm suggesting go read about ethics IN YOUR FIELD. Ethics is part of that field. So go learn it in the same way you keep up to date on ANYTHING that is new or you don't know!
In honor of PhD application season winding down, here's a tweet-thread-that-should-probably-be-a-blog-post on things I have observed through hearing from a LOT of PhD applicants in many different fields over the past eight months. TL;DR this process can be better. 🧵
Consider things admissions committees or faculty might expect to see in an application: LORs from certain types of people that mention certain things, statements of purpose with certain elements (faculty mentioned, why this program, etc.)
Are applicants TOLD these expectations?
As one critical example, are you in a field where it is common or there is even an expectation that applicants reach out to potential advisors before they submit an application? Is there any reason applicants would know this if they don't already have mentorship in that field?
This thread is for live-tweeting the ethics session at #SIGCSE2021. FIVE papers at @SIGCSE_TS this year about ethics in computer science education! 🧵
First up: "How Students in Computing-Related Majors Distinguish Social Implications of Technology" by Diandra Prioleau et al. at University of Florida.
They presented students with scenarios about AI technology (e.g. recidivism algorithms)...
... and found that their participants could spot social implications, but frequently missed issues of systemic discrimination. But surprisingly: About half of students had never heard of these issues, which points to a gap in computing curriculum. dl.acm.org/doi/10.1145/34…