The Reddit Blackout: An Initial Exploration with Support-Seeking Subreddits

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Reddit is no stranger to conflict between its users, but in its most recent controversy, the company found itself playing the antagonist. In June 2023, Reddit made headlines for being the subject of one of the largest scale protests by users of a site the Reddit blackout. However, not much is known about the effects of the event on Reddit’s communities. This semester, I began exploring how the culture of support-seeking subreddits was impacted by the blackout.

Image from the Wikimedia Commons

The Reddit Blackout

On June 12, 2023, more than 7,000 communities on Reddit went private — making them inaccessible to non-subscribers. The collective disabling and restricting of subreddits is known as a Reddit “blackout.” The decision to organize this blackout was largely made in protest of the company’s decision to charge for API access. Moderators fulfill their duties mainly by relying upon third party apps that were built using Reddit’s API. By fixing a price for the API that popular third party developers could not possibly afford, Reddit was essentially shutting down these apps. Moreover, by shutting down these apps, Reddit was ignoring the needs of moderators, arguably their most important users. 

In order to remind Reddit of their importance, moderators came together to devise a blackout of unprecedented scale. Subreddits went private for 48 hours. Hopefully, the company would recognize how much they needed their moderators and give in to their demand of reducing the price of API access. 

Unfortunately, this objective was not met. Reddit was determined to remain faithful to its business decision and wait out the blackout. Moderators were also unwilling to back down. After the originally planned 48-hour protest period was over, many subreddits remained private. The company then began to antagonize moderators by threatening to remove them and forcing subreddits to reopen. 

While some subreddits remain private even today, the blackout largely came to an unsuccessful end. The company was able to force many subreddits back into some form of normalcy, but community sentiment towards management has never been lower. In a post about the blackout, a moderator said, “I believe that Reddit administration has demonstrated an unsurprising but none-the-less disappointing lack of foresight and understanding of how their website operates… I believe [they do] not understand the value that their unpaid moderators bring to the website” (“[Modpost] Reddit Blackout – What’s Happening Next,” 2023).  Moderators feel that Reddit’s actions have made it abundantly clear how little the company cares about their users’ perspectives and moderator labor. 

This semester, I wanted to understand how the Reddit Blackout affected (1) Reddit as a community nurtured by volunteers and (2) Reddit as a dataset. The following questions guided my work:

  • How do people use Reddit to seek support?
  • What does current research say about the struggles of Reddit moderators?
  • Now that API access is gone, is running a large-scale data analysis about the blackout feasible?

Support Seeking on Reddit

Social support is the receipt of help from others by an individual (Zou 2024). Reddit is a social media platform structured into subject-specific communities (called subreddits) where users can post and interact. This topic-specificity makes Reddit a convenient venue for seeking social support. In fact, it has been praised for hosting certain support-seeking communities, particularly those serving people attempting sobriety (Sowles 2017). Subreddits that support drug recovery are just one of many support-giving communities. Redditors can receive emotional, informational, and tangible social support through the platform (Zou 2024). 

Moderator Labor

Volunteer moderators are an integral part of the culture and maintenance of Reddit. However, moderators’ important labor is often underappreciated due to misconceptions regarding what they do. These misconceptions largely stem from two main problems: (1) the lack of visibility around much of moderators’ contributions (Li 2022) and (2) a heightened focus on controversial tasks that they are seldom directly responsible for (Gilbert 2020). 

Reddit is designed such that comment removal is highly conspicuous (comments removed by moderators are replaced with the text “[deleted]”), promoting the idea that moderators’ main service is censoring users (Gilbert 2020). However, the majority of comment and post removal is actually performed by bots (Li 2022). So while moderators do find and implement technical workarounds, such as bots, they do not typically perform removals themselves. The misconception that moderators censor users results in community backlash and undue emotional burden on moderators (Gilbert 2020). Moderators also have over 64 other non-removal actions they are responsible for (Li 2022). Unfortunately, a recent study found that approximately 43% of their extensive labor is essentially “invisible” (Li 2022). 

The misunderstood and unseen labor of moderators complicates their relationship with Reddit as a company. The value and legitimacy of labor is typically correlated with its level of visibility (Gilbert 2020), so moderators are in a position of disadvantage at the negotiation table with the company (Li 2022). The company’s misconception of the labor of their moderators allows them to neglect the volunteers that keep their platform usable and prioritize investing in what they think will maximize revenue generation. This has been the root cause behind the major “blackouts” that the platform has experienced (Matias 2016). 

Data Exploration and Struggles

Given Reddit’s decision to charge for API access, obtaining subreddit data is not as straightforward as it once was. Fortunately, I was able to find a post download tool that enabled us to retrieve data from several support-seeking subreddits. 

The subreddit I initially chose to focus on was r/Depression. I analyzed a total of 115,093 r/Depression posts from April 2023 to February 2024. As I was looking through r/Depression posts during the original 48 hour period of the blackout, I realized that no one was mentioning the blackout. The subreddit hadn’t participated in the blackout, but I was still surprised that there were zero references to it. Both during and in the week after the blackout, the most frequently used words were the epitaphs “[removed]” and “[deleted].” I wasn’t sure if this meant that discussion around the blackout had been expunged or that there had never been any discussion around it at all. This made it very difficult to find patterns in posts and sentiments from during the blackout by members of the r/Depression subreddit.

(Left) Plot of r/Depression posts by day, in which June 12th, the first day of the blackout, is circled in red. It had the most posts of the month. (Right) Top 10 most frequently used words in posts during the blackout. The epitaphs [deleted] and [removed] were most common.

I then chose to redirect my attention to a subreddit that actually participated in the blackout, hoping this would make lack of discussion around the blackout unlikely. I looked at 29,229 r/SocialAnxiety posts from January to December 2023. There were no posts on the subreddit from during the blackout dates. I was not sure if that meant they all got deleted, or if posts from when a subreddit was private are not accessible via the post download tool. Either way, it was clear I needed to adopt a new approach to understand the blackout’s impact. 

Planned Quasi-Causal Analysis

After conducting some preliminary exploration of Reddit data from the blackout period, I became interested in determining the effects of the blackout on the culture of supporting-seeking subreddits. Specifically, I want to look at the blackout as an intervention and perform a comparative analysis on the culture of these subreddits before and after the blackout. To do so, I am going to use Regression Discontinuity in Time (RDiT), which we have seen applied successfully in similar work on Wikipedia (Hill 2021). 

RDiT is a quasi-causal method that compares regressions before and after an intervention date in order to identify causal effects. The intervention dates will be the initial 48 hour blackout period from June 12, 2023 to June 14, 2023. RDiT is a useful method for the data given that it relies on normalized intervention dates rather than a standard intervention score. RDiT also controls for fluctuations that organically occur over time, ensuring the comparison is strictly in relation to the intervention. This semester, I selected 12 candidate subreddits for analysis.

Selecting Subreddits for Analysis

I want to use data from support-seeking subreddits for my quasi-causal analysis. In particular, I want to analyze support-seeking subreddits across four categories: mental health subreddits that participated in the blackout, mental health subreddits that did not participate in the blackout, non mental health subreddits that participated in the blackout, and non mental health subreddits that did not participate in the blackout. I want to understand whether the intervention had an effect on participating subreddits by looking at how it affected both participating and non participating subreddits. I also want to grasp how the cultures of mental health support-seeking subreddits specifically were impacted by decisions to become private or restricted. After all, these subreddits can be especially critical for people in crises, making their shutdowns potentially more damaging than others’ for consumers of Reddit. 

Table of the 12 subreddits selected for the quasi-causal analysis.

Selecting subreddits within the four categories faced three main challenges: they each had to be support-seeking, have around the same number of members as their counterparts, and be available for research and download. Furthermore, each subreddit had to be investigated to determine whether they participated in the blackout or not. Initially, a post on the r/ModCoord subreddit that listed all blackout-participating subreddits was consulted. From here, after manual inspection of member numbers, the following 6 subreddits were selected that participated in the blackout: r/BPD, r/Autism, r/SocialAnxiety (3 mental health subreddits), and r/Confidence, r/LearnMath, and r/MaleHairAdvice (3 support-seeking but not mental health related subreddits). Manual inspection of subreddits by size on the “Top Communities” pages of Reddit yielded 6 more subreddits. These 6 subreddits did not participate in the blackout: r/CPTSD, r/Lonely, r/MentalHealth (3 mental health subreddits), r/FreeFood, r/NeedAFriend, and r/Texts (3 support-seeking but not mental health-related subreddits). Together, these 12 supporting-seeking subreddits form the dataset upon which I will be conducting my quasi-causal analysis. 

Closing Remarks

The Reddit blackout remains one of the largest and best documented online protests. Although the disruption appeared to have little impact on Reddit’s business decision, its consequences for the people who rely on Reddit’s communities for social support are unexplored. Next semester, I look forward to better understanding how online collective action changes support-seeking communities long term. 


Gilbert, S. A. (2020). “I run the world’s largest historical outreach project and it’s on a cesspool of a website.” Moderating a Public Scholarship Site on Reddit: A Case Study of r/AskHistorians. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), 1–27.

Hill, B. M., & Shaw, A. (2021). The Hidden Costs of Requiring Accounts: Quasi-Experimental Evidence From Peer Production. Communication Research, 48(6), 771-795.

Li, H., Hecht, B., & Chancellor, S. (2022). All That’s Happening Behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 584-595.

Matias, J.. (2016). Going Dark: Social Factors in Collective Action Against Platform Operators in the Reddit Blackout. 1138-1151. 10.1145/2858036.2858391. 

Morrison, S. (2023, June 20). Reddit blackout: What is it and why are subreddits going dark? Vox.

Peters, J. (2023, June 30). How Reddit crushed the biggest protest in its history. The Verge.

R/3d6 on Reddit: [Modpost] Reddit Blackout – What’s Happening Next? Reddit. (n.d.). 

Sowles, S. J., Krauss, M. J., Gebremedhn, L., & Cavazos-Rehg, P. A. (2017). “I Feel Like I’ve Hit the Bottom and have no Idea what to Do”: Supportive Social Networking on Reddit for Individuals with a Desire to Quit Cannabis Use. Substance Abuse, 38(4), 477–482.

Zou, W., Tang, L., Zhou, M., & Zhang, X. (2024). Self-disclosure and received social support among women experiencing infertility on reddit: A natural language processing approach. Computers in Human Behavior, 154, 108159-.

How do relationship conflicts look from the other side? Here are answers from body-swapping in VR

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Most people have ample experience with personal conflicts, whether it be a disagreement with your significant other, your mom, or just a really close friend. And most would agree that they are extra tricky to deal with: as seen in the 4-panel comic above, the real issue in this couple’s argument is not actually about the pizza. Just like how for arguments over who does the dishes at home, it’s usually not just about the dishes. Personal conflicts can involve differences in perspective that run deeper in the relationship and are hard to resolve via surface-level conversation.

To really enable a change in perspective for those stuck in personal conflict, we propose and evaluate an autobiographically-accurate retrospective embodied perspective-taking system based in VR that enables users to immersively re-experience a past conflict interaction as their partner, essentially
“body swapping”:

We conducted a mixed-methods controlled study with 26 couples to compare the types of insights and changes in conflict behavior evoked by our “body swapping” approach to the current industry practice of video recall—rewatching footage of both partners in a conversation.

We found that the experience of retrospective embodied perspective-taking led individuals who were in conflict with their significant other to develop transformative insights constituting major changes in opinion about their partner, themselves, and even the issues of conflict. One woman mentioned how the experience changed a negative view she had of her husband which had persisted throughout 10 years of their marriage prior to the study:

“I found a lot of value in watching his hands. My husband does a lot of repetitive hand movements when he’s nervous, and it tends to frustrate me, and make me feel like he is uncomfortable with what I’m saying. Watching him do it from his perspective, I felt uncomfortable vs. frustrated. Seeing myself talk to him the way I did, I can now understand why he would make those kinds of gestures because even ‘I’ was nervous with how absolute and sure I was when speaking to him.

I think my biggest realization is that I thought my husband was the major reason that we had trouble communicating. And while he might not like conflict, I spend a lot of time saying what he’s doing, versus what I’m doing. I have taken this approach to this conversation so many times, and hearing/watching myself from this point of view makes me think about how many times my partner has been on the receiving end of me pointing out things and for me, doing that it felt like, here we go again, but not from my standpoint, from his standpoint — of like, here she goes again.

Our findings showed that addressing personal conflicts isn’t always about talking through the details of an issue — VR-enabled body swapping can help people understand what others are actually thinking and experiencing, which gets at the personal perspectives at the core of conflict in close others.

Want to see the full story on how embodied perspective-taking impacts conflict in close relationships? Check out our paper, or come watch my in-person talk on May 13, 2024 at 4:30pm Hawaii time!

Seraphina Yong, Leo Cui, Evan Suma Rosenberg, and Svetlana Yarosh. 2024. A Change of Scenery: Transformative Insights from Retrospective VR Embodied Perspective-Taking of Conflict With a Close Other. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 18 pages.

Reflecting on Consent at Scale

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In the era of internet research, everyone is a participant.

A PhD stood at the front of a crowded conference hall.  They’d just presented their paper on social capital in distributed online communities. As the applause settled, an audience member scuttled to the microphone, eager to ask the first question.

A professor from University College. Thank you for the great talk. It was refreshing to attend a talk with such rigorous methods. You scrapped data from so many different subreddits and made such a compelling argument for how these results will generalize to other online spaces. My question is less about the research and more about your experiences with data contributors. How did the various subreddit community members react when you talked to them about this exciting work?

What kind of question is this? The PhD thinks to themself. It’s not feasible to get consent from every user. We got an IRB exemption, got approval from subreddit moderators, and followed all the API terms of use and regulations for researcher access. Do other researchers really ask for consent at scale? Did I get consent…?

You may be in a similar situation now! Using social media data for research is a common method that has massive potential for large-scale analyses in both quantitative and qualitative research. However, it can be frustrating to simultaneously hold individual, affirmative consent as the golden standard and recognize its limitations as a viable option for many researchers. To that end, we’ve made a reading list about getting individual consent at scale, particularly in research settings. We hope this reading list serves as a provocation for discussion rather than a list of solutions to this problem.

Normative Papers

1. The “Ought-Is” Problem: An Implementation Science Framework for Translating Ethical Norms into Practice. Our resident ethicist (Leah Ajmani) loves this paper so much! It basically uses informed consent as a case to describe the larger translational effort needed to move from normative prescriptions to actual implementation.

2. Yes: Affirmative Consent as a Theoretical Framework for Understanding and Imagining Social Platforms. A contemporary classic in CHI,  this paper does a really good job of describing affirmative consent as the ideal situation but then using the “ideal” for explanatory and generative purposes. There is merit to having an ideal, even if it is not perfectly attainable!

HCML Papers

We’re obviously biased because she’s a GroupLenser, but Stevie Chancellor does a great job at describing consent at scale as an ethical tension rather than a “must-have.” It is something researchers need to navigate with justified reasoning.

1. A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media

2. Toward Practices for Human-Centered Machine Learning

Design Papers

These papers are both critical of current consent design and do a great job of discussing alternatives, even if it is outside of a research context.

1. (Un)informed Consent: Studying GDPR Consent Notices in the Field

2. Limits of Individual Consent and Models of Distributed Consent in Online Social Networks

From grappling with moral nuance to designing better consent procedures, these readings can take our discussions of individual consent at scale from a theoretical ideal to an operationalizable goal. So, let’s embrace difficult discourse about how to move forward and continue to traverse the space between the idyllic and the feasible. Comment or tweet which papers you would add to this list!

Wordy Writer Survival Guide: How to Make Academic Writing More Accessible

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As GroupLensers received CHI reviews back, many of us were told our papers were “long,” “inaccessible,” and even “bloated.” These critiques are fair. Human-Computer Interaction (HCI) research should be written for a broad and interdisciplinary audience. However, inaccessible writing can be hard to fix, especially if it is your natural writing style. Here’s some advice from GroupLens’s very own Stevie Chancellor (computer science professor, PhD advisor, and blogger about everything writing-related)

Sentence Structure

  • Sentence Length: How long are your sentences, and how many comma-dependent clauses are going on per paragraph? Long sentences are more complicated to read and, therefore, harder to parse. Some people say to split any sentence with more than 25 words. Eh. 30-35 should be fine for academic writing, but longer is worser. 
  • Commas, Commas, Commas: Comma-separated clauses are painful to follow. A comma is a half-stop in writing and momentarily pauses trains of thought. While some commas are grammatically necessary (see the one that follows this parenthesis), too many commas chop your sentences into pieces. Therefore, too many commas interrupt your reader’s comprehension of your idea.
  • Sentence Cadence: How are you varying your cadence of the writing? Do you use short sentences, then longer sentences, and vary the structure and placement of comma clauses? Using ONLY long sentences gets repetitive and, therefore, more challenging to read.
  • Topic Sentence and Transition Clarity: Topic and “transition” sentences should be crystal clear in their simplicity. Interior sentences can be more elaborate/have more “meat.”

Word Choice

  • Simple Words are Better: Are we using as simple words as possible to describe what we mean? For example: do not write “utilize” as a synonym for “use”. Just say “use”. 
  • Active vs. Passive Voice: Are you overly using the passive voice and not active? Passive voice is occasionally correct, especially when needed to soften a claim (e.g., “Research has suggested that….”). But too much passive voice is hard to read.
  • Filler Words: Look for words that contribute nothing to the idea but make your sentence longer. Adverbs and fluffy adjectives are common culprits of this. Adverbs like “very”, “fairly”, and “clearly” provide almost NO substance to writing but lengthen the sentence.
  • Weasel Words: Inspired by Matt Might, check your writing for “weasel words” that augment the clarity of your sentence. Do you need to say an experiment was “mostly successful, but had limitations?” Or can you say, “The experiment was successful in X and Y with less success in Z”?
  • Citations vs. Names: Be judicious with \citet{} in your writing. Invoking someone’s name is equivalent to inviting that person to a dinner party and forces the reader to pay attention to the “who’s who” of your writing. Who do you want to invite to your home? Remember, you’re in charge of maintaining conversation during the party and providing food for everyone, so be careful who you invite.

Pragmatic Decisions/Actions

  • Read Aloud: Read “dense” or “inaccessible” sections out loud. Say them with your mouth. Long, poorly structured paragraphs become obvious when read out loud.
  • Use a Friend or Colleague To Kill Your Darlings: Friends and colleagues with no emotional connection to the paper are great for removing self-indulgent yet non-essential writing. Ask a friend to read a section to go in and “kill your darlings.”
  • Use AI Tools Judiciously: Tools such as Grammarly Pro, Writefull, or ChatGPT/Bard/LLM du jour can do first passes for wordiness and phrasing. For example, Grammarly Premium provides swaps for too-long phrases (and is free if you have a SIGCHI membership). LLMs can trim your writing by 10%. Just be cautious in the accuracy of the edits and maintain the same tone and argumentation.
  • Ctrl + F Is Your Friend: Recognize your writing “quirks” and ctrl + f to search for and cut them. Stevie’s writing quirks include using adverbs in initial drafts, meaning that searching for “very” and “ly” returns many words to cut.

From managing sentence structure to choosing simple words, these tips can take your writing from “in the clouds” to a reader-friendly and enjoyable experience. Remember, the goal is not just brevity but clarity, ensuring that our work resonates with a broad and interdisciplinary audience. So, let’s embrace these tips, Ctrl + F our way through, and invite our readers to a well-organized and engaging intellectual dinner party. Cheers to more accessible and impactful HCI research!

Page Protection: The Blunt Instrument of Wikipedia

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Wikipedia is a 22 year-old, wonky, online encyclopedia that we’ve all used at some point. Currently (2023), Wikipedia has a dizzying amount of information in numerous languages. The English language of Wikipedia alone has over 6 million articles and 40,000 active editors. The allure of Wikipedia articles is that they are highly formatted and community-governed; while anyone can contribute to a Wikipedia article, there’s a vast infrastructure of admins, experienced editors, and bots who maintain the platform’s integrity. Wikipedia’s About page reads:

Anyone can edit Wikipedia’s text, references, and images. What is written is more important than who writes it. The content must conform with Wikipedia’s policies, including being verifiable by published sources […] experienced editors watch and patrol bad edits.”

Our research aims to understand the tension between open participation and information quality that underlies Wikipedia’s moderation strategy. In other words, how does maintaining Wikipedia as a factual encyclopedia conflict with the value of free and open knowledge? Specifically, we look at page protection–an intervention where administrators can “lock” articles to prevent unregistered or inexperienced editors from contributing.

We used quasi-causal methods to explore the effects of page protection. Specifically, we created two datasets: (1) a “treatment set” of page-protected articles and (2) a “control set” of unprotected articles that were similar to a treated article in terms of article activity, visibility, and topic. We then ask: does page protection affect editor engagement consistently?

Our findings show that page protection dramatically but unpredictably affects Wikipedia editor engagement. Above is the kernel density estimate (KDE) of the difference between the number of editors before page protection versus after protection. We evaluated this metric across three time windows: seven, fourteen, and thirty days. Not only is this spread huge, but it also spans both a negative and positive difference. In essence, we cannot predict whether page protection decreases or increases the number of people editing an article. 

Are heavy-handed moderation interventions necessary for a complex platform such as Wikipedia? How can we design these non-democratic means of control to maintain a participatory nature? Check out our paper for discussions on these questions or come to my talk on October 16, 2023 at 4:30pm!