Welcome to our presentation of
"I Will Not Drink With You Today": A Topic Guided Thematic Analysis of Addiction Recovery on Reddit.
I'm Robert P. Gauthier, presenting this paper
on behalf of my co-authors Mary Jean Costello and James R Wallace.
Reddit communities are playing a growing role in addiction recovery.
But recovery is a complex journey. And it requires a strong network of peers
to help individuals move towards a healthy, productive, and meaningful life.
So do we really know what it means to be a "support group in your pocket"?
To better understand these communities, we performed a topic guided thematic analysis
of posts on two subreddits, r/stopdrinking and r/OpiatesRecovery.
We focused our analysis to answer two research questions.
1) How are stories used for addiction recovery in these Reddit communities,
2) How do community members support each other's recoveries?
There are three key takeaways from our paper.
First is how people's addiction recovery journeys are being aided by these communities as they
share lived experiences, emotional support, and help people navigate traditional recovery resources.
Second, is our discussion of how the holistic understandings from these descriptions can
inform practice for mutual aid programs, healthcare practitioners, and HCI researchers. They can
act as an experiential knowledge pool and provide a route for us to empathize and consider
how best to support people on addiction recovery journeys.
Third, we show through application and reflection how computation techniques can support the
development of qualitative understandings of online communities.
For the sake of time, this presentation will be focusing on takeaways one in three.
Please see our paper for full details.
To perform our topic guided thematic analysis, we first collected data.
We used pushshift.io to download the submissions and comments
for both r/stopdrinking and r/OpiatesRecovery
We recreated threads by merging the submissions and comments for each subreddit using thread IDs.
We then performed Latent Dirichlet Allocation topic modeling on each of the subreddits.
During this process, we would clean our data, build models, evaluate the models,
and perform further iterations of cleaning, building, and evaluation
as we boosted signals, reduced noise, and tuned our models parameters.
We ended up with a model for each of the subreddits.
Using these models, we generated proposal samples which consisted of
the 16 topics from each of the two models, keywords associated with each of the topics,
and 20 Representative threads from each of the topics,
ending up with 320 threads from r/stopdrinking and 320 threads from r/OpiatesRecovery.
We then performed a reflexive thematic analysis, which leverages the proposal samples we generated
as well as the familiarity we gained with the data through the topic modeling process.
For example, we encountered an acronym that identified a reoccurring phrase "I will not
drink with you today", and started coalescing the idea of the story it was telling us about the communities.
We continue to build on our familiarity by reading and rereading our propulsive samples
as well as the broader subreddits.
We then used our familiarity to inductively identify codes in our data.
We took these codes and we developed them into themes.
We then reviewed the themes against both the proposal samples and the broader subreddits.
And we'd iterate allowing us to gain more familiarity, revise our codes, revise our themes,
and ended up generating a result that is two sets of themes grouped under our two research questions.
To summarize our themes,
these communities are acting as channels aiding in addiction recovery journeys,
people are able to share lived experiences through their personal stories
describing their successes, failures, and the consequences of addiction and recovery.
They're a place where people can access a network of peers who can share advice on common problems
and provide emotional support through solidarity phrases like "I will not drink with you today".
And encourage one another to continue their recovery journey string both good and bad times.
Communities also provide a place to discuss traditional recovery resources and their challenges.
For example, here's a paraphrase submission from r/OpiatesRecovery.
Why are good meetings so hard to find?
In the submission, the poster is expressing multiple concerns.
They're feeling like they're having trouble finding a meeting that they fit with.
They're feeling like they're having trouble finding a meeting they feel safe going to.
They're having trouble with using the finder app to find an appropriate meeting.
And they're feeling rejected by the broader addiction community.
In response to this post, the community stepped up.
They provided suggestions about how to broaden the search and use different apps.
They gave some guidance on what meeting might be a better fit.
They discussed how to phrase language differently to avoid the stigmas associated with opioid addiction.
And last, but certainly not least, they encourage the poster
to continue their efforts to use in-person recovery resources.
These discussions of traditional support were not limited to how to access it.
They also discussed how to navigate challenges as experienced within that support.
For example, here's a paraphrased submission and comment from r/stopdrinking.
The original poster is concerned about references to God and a higher power
because religion just isn't for them.
In response, the community provided suggestions
such as reconsidering what higher power and God mean in non-religious contexts.
They also suggested alternative programs like SMART and gave advice to allow the person
to continue to benefit from the traditional recovery resources.
Next, we reflect on our method.
First, we found the work performed as part of the LDA modeling process informed our thematic analysis
for example, tokenization and filtering helped us identify key phrases,
such as "I will not drink with you today".
However, we think the qualitative tasks, such as coding, could help direct the modeling,
which in turn might further help guide the thematic analysis.
Next, we found purposive sampling based on generated topics
allowed us to focus on phenomena that were already tightly grouped.
However, it is easy to fall into the trap of over-optimizing,
now that we are using both topic modeling and thematic analysis.
Rather, we need to view both topic modeling and thematic analysis as interpretive tools
that would benefit from scaffolding that's designed to support qualitative analysis.
In pursuit of these opportunities,
we developed the Computational Thematic Analysis Toolkit, which we recently published a Group 2022.
And we encourage you to please take a look at that paper for more recent developments.
In answer to our original question, we now have a better understanding of what it means to
be "a support group in your pocket".
From our paper, we have a description of how Reddit communities are sharing lived experiences,
emotional support, and helping people navigate traditional recovery resources
on their addiction recovery journeys.
We discuss how holistic understandings from these communities can inform practice
for mutual aid programs, healthcare practitioners, and HCI researchers,
all who seek to aid people seeking out addiction recovery.
And we applied and reflect on how computational techniques can support the development of
qualitative understandings of online communities.
This work was made possible by the University of Waterloo, a NSERC Discovery Grant
and an OGS scholarship
We'd like to thank the game's Institute, Waterloo HCI, and all of our reviewers for the valuable feedback.