A little over a month ago, in June 2026, we sent out surveys to members of the Reddit community, r/AmItheAsshole. Since we can’t fit all things we’d like to share with the community in 3k characters (which is the max length the community allows) we’ve drafted this companion report to provide details about our goals for the survey, our methods and ethics, and dive deeper into the results. 

Study Goals

Our survey has two main purposes: one is a digital census of the community, to provide insight into who’s participating, how, and why. The other purpose is to answer some scientific questions that we have about community norms, participation, and technology (e.g., Reddit’s recommendation systems, generative AI, google search results). Essentially, the rules and norms of online communities shape why and in what capacity community members participate. Recommender systems, for example, determine what information we see and who we interact with. We wanted to know the impact of systems like these on how people interact with the community, what their motivations for coming to a given community are, how aligned their motivations are with how mods understand the community, and how they behave (do they follow the rules? Do they experience some kind of sanction, like a comment removal or ban?). More details about the scientific questions and models we’ll be testing can be read in our pre-registration and you can see other details about the study, including reports from other community surveys on the project page.  

We’ll answer the scientific questions by analyzing survey data from across multiple communities, so aren’t sharing that yet, but we can share results about r/AmItheAsshole! 

Methods

Because behavior in a community is part of our research questions, and because self-reported behavioral data can be pretty unreliable, we wanted to survey people based on their actual participation in r/AmItheAsshole (AITA from here on out). To do this, the modteam agreed to let me use the Lab’s bot, u/civilservantbot, to log data from the subreddit and the modlog. This allowed us to identify active members of their community. We used 6 months of historical data to randomly select from users who participated at least once in the following ways:

1. People who have made a post or commented without experiencing content removals or suspensions (we refer to this group as Unsanctioned)
2. People who have had a post or comment removed but not suspended (we refer to this group as Removals);
3. People who have been suspended, with either a temporary or permanent ban (we refer to this group as Bans).
4. Collectively, we refer to this group as our Random Sample. 

Based on response rates from pilot testing and launching a similar survey with r/AskHistorians, and after conducting a power analysis, we sampled the following number of people, shown in the table below, which also includes the number of surveys we got from each group. Something to note is that we expect that there are some number of accounts we messaged that are either bots and/or have been banned from Reddit, particularly among the Bans group, so we’re not ready to report response rates yet. 

Group	Total Accounts	Messaged Accounts	Surveys finished
Unsanctioned	291747	7548	321
Removals	71418	7526	268
Bans	8776	7522	87
Posts	NA	NA	60
Ads	NA	NA	34

The bot was then used to send private messages to everyone included in the random sample. We sent one initial message and then one reminder a week later.

However, this recruiting method completely omits lurkers. To try to get insights from lurkers, we also distributed the survey through ads and a public post (numbers also included in the table above). It’s important to note that these participants “self-selected” to take the survey rather than being randomly sampled.

If you’re curious about the survey questions we asked, you can view an almost identical survey here.

We designed this study to be as representative as possible of the people who have participated in the community in the past six months, but there are some important things to keep in mind when interpreting the results:

🟢 High Confidence: Behavioral Experiences

• Our sampling strategy used behavioral data, so we are confident that our results reflect the experiences of the different groups we sampled: banned users and posters with and without removals. Generally speaking, you can treat reports on how these groups perceive rules, see and engage with the community as solid estimates for these specific populations over the last six months.

🟡 Moderate Confidence: Sociodemographics (Race, Gender, etc.):

• There’s no master census of Reddit, so we cannot be 100% sure if our respondents’ demographics perfectly mirror the entire subreddit. We can accurately report the demographics of our sample, but while we expect the community at large to look similar, we cannot claim these numbers are a perfect 1:1 match for the sub’s 24 million members. This is the best estimate possible given the privacy of the platform.

🔴 Descriptive Only: Ads and Public Posts

• The data from ads and the public post comes from self-selected samples. We don’t have historical log data for these users (many of whom are lurkers), so we don’t know how they differ from the rest of our strata, nor how they differ from those who saw the ad but didn’t click. These results are descriptive only. They offer a great snapshot of those specific respondents but should not be used to make broad, definitive claims about all lurkers in AITA. 

We did our best to check for trolls, bots and insincere responses. We flagged any responses that completed the survey too fast, or too slow, or who provided implausible responses (e.g. age = 150). The majority of flagged responses were removed during our cleaning process due to missing responses. Finally, we manually checked any remaining flagged respondents who were eligible for our analytical sample to decide whether to keep or remove the observation. Our review resulted in 11 surveys being removed from the analysis. 

Ethics

The survey was approved by Cornell’s Institutional Review Board (IRB) IRB0149466. However, the public post surfaced two ethics questions that we’d like to address here too: 

Data sharing with the United States Government 

The informed consent form contained the following text:

We will do our best to keep your participation in this research study confidential to the extent permitted by law; however, it is possible that other people may need to review the research records and may find out about your participation in this study. For example, the following people/groups may check and copy records about this research: The Office for Human Research Protections in the U. S. Department of Health and Human Services

This text was understandably alarming to some people, especially since some folks interpreted it as though we were planning to hand over the data to the US government. That is not the case. 

To clarify, this is language that is required by Cornell’s IRB for studies that are funded by the United States government (e.g., the National Science Foundation) and recommended for all other studies. This study was funded by a philanthropic organization called the Templeton World Charity Foundation, not the US government. 

We do not anticipate that the US government will ever request our data, but if they do, we’ll fight it. We also didn’t collect any personally identifiable information, responses are not connected to anyone’s user account and none of the questions ask for information that’s sensitive. So the risk of the government getting information, let alone information that could be traced to an individual, let alone accessing information about an individual that reveals much about them other than how they participate in AITA is very, very low. 

However, while we may see the risk as very very low, taking the risk is not our decision to make. So, we opted to include the recommended language on the informed consent form, so that consent could be truly informed.

Messaging 

I (Sarah) received some complaints in the public post and via DM that viewed sending unsolicited messages to people with the survey as spamming; however, the messages weren’t in violation of Reddit’s rules on spamming. There was also a question about how this was allowed by Cornell’s IRB. Sending unsolicited messages is pretty standard practice for survey recruiting and ultimately less invasive than more traditional forms of recruiting, like calling you on the telephone at random or even coming to your door. I can’t speak for the IRB, but I suspect this was viewed as a fairly routine recruitment method, especially since the data collected by the surveys can never be connected to the person to whom the link was sent. I certainly didn’t think it would be more than mildly annoying at worst, so I’d like to apologize to anyone who received the messages and was upset by them.  

Results

Here we show a quick snapshot of our data. Keep in mind that we are only showing information of people who are part of our “analytical sample,” that is, people who have fully answered all the questions that go into our statistical models. This results in a sample of 158 banned users, 320 rule abiders, and 264 sanctioned users. We also have 34 respondents from the ads and 58 from the public post, for a total of 834 respondents. Throughout the post, results noted in the write-ups are highlighted via bold text in the tables.

Sample	Count	% Sample
Unsanctioned	320	43.13%
Bans	158	21.29%
Removals	264	35.58%
Overall Random Sample	742	100%
Post	58	100%
Ad	34	100%

Demographics

First we’ll share results from the demographic questions we asked, including gender, age, minority group status, education, and location. 

Gender

Sample	Strata	Woman	Man	Gender Diverse	Prefer not to say
Random Sample	Bans	42.04%	42.04%	7.01%	8.92%
Random Sample	Removals	48.48%	37.88%	7.2%	6.44%
Random Sample	Unsanctioned	57.99%	29.15%	7.52%	5.33%
Random Sample	Overall	51.22%	35.29%	7.3%	6.49%
Ad	n/a	50%	35.29%	2.94%	11.76%
Post	n/a	79.31%	12.07%	5.17%	3.45%

Looking at the overall random sample, women accounted for roughly half of the responses (51%), as they did for the ads too; however, within the strata there is a higher percentage of women in the unsanctioned group (58%) and fewer women in the ban group (42%). Far more women responded to the survey distributed via the public post (79%) than did other genders. 

Age & Minority Group

Sample	Strata	% in minority group	Mean Age	Age Std	Min Age	Max Age
Random Sample	Bans	42.95%	39.53	13.76	18	73
Random Sample	Removals	43.94%	40.84	13.31	18	90
Random Sample	Unsanctioned	44.83%	40.38	12.16	18	73
Random Sample	Overall	44.11%	40.36	12.92	18	90
Ad	n/a	47.06%	30.69	11.31	18	57
Post	n/a	37.93%	41.02	12.77	19	72

The average age was also fairly consistent across samples with a mean of around 40 years old. However, survey respondents from the ad tended to be quite a bit younger, with a mean age of 31 years. A relatively high percentage of our respondents across samples identified as a member of a minority group (between 30%-and 47%). Below, we share a word cloud of the most common self-identified minority groups. We’re reporting percentages only and results from across the random sample to preserve privacy due to relatively low numbers of certain groups. 

Education

Sample	Strata	Less than High School	High school to some college	Associate or trade degree	Bachelor’s degree	Advanced degree
Random Sample	Bans	1.28%	30.12%	11.54%	26.28%	30.77%
Random Sample	Removals	1.14%	22.81%	12.69%	33.84%	28.52%
Random Sample	Unsanctioned	1.25%	20.94%	12.5%	32.19%	33.12%
Random Sample	Overall	1.22%	23.55%	12.72%	31.53%	30.99%
Ad	n/a	0	29.41%	5.88%	29.41%	35.29%
Post	n/a	0	15.52%	1.72%	34.48%	48.28%

Survey respondents were highly educated—across each sample and within each strata at least two thirds had a Bachelor’s or advanced degree. That so few respondents have less than a high school degree is in part a reflection of our survey, which was limited to people 18 years of age and older as we did not have ethics approval to collect data from minors. 

Location: top three

Location	% of Random Sample	% of Post Sample	% of Ad Sample
United States	67.75%	68.42%	6.7%
Canada	8.25%	14.04%	0
UK and Ireland	6.04%	5.2%6	<5%
Bangladesh	0	0	10%
Colombia	0	0	10%
India	<5%	0	10%

Survey respondents from the random and post samples were mostly from the United States, followed by Canada and the UK and Ireland. While it’s hard to find good statistics about all Reddit users’ location, our results are not dissimilar from desktop users (most of whom are also from the US, UK, and Canada). That the population of respondents from the ads is so different is a function of how the ad system on Reddit works. To have an ad shown to different groups, advertisers need to place a bid and compete with other advertisers who want to advertise to the same audience. By whatever metrics Reddit uses, r/AmItheAsshole users and residents of the United States tend to be fairly expensive. 

Subreddit use

Most people who responded to the survey were subreddit subscribers:  

• 74.8% of the Random Sample 
• 67.65% of the Ad Sample 
• 56.9% of the Post Sample 

When we look at the breakdown within the random sample, we see a slight (predictable) trend between subscribership and following the rules. 

• 80.62% of unsanctioned users
• 78.03%  of users with removals
• 57.59% of banned users. 

First visit

Sample	Strata	Less than 6 months	Between 6 months and 5 years	More than 5 years
Random Sample	Bans	18.35%	50%	31.65%
Random Sample	Removals	9.47%	61.36%	29.17%
Random Sample	Unsanctioned	10.31%	55.62%	34.06%
Random Sample	Overall	11.73%	56.47%	31.81%
Ad	n/a	11.76%	61.76%	26.47%
Post	n/a	8.62%	60.34%	31.03%

Our respondents also tended to be long-time users, with the plurality in each group reporting their first visit between 6 months and 5 years ago. Unsanctioned users were the group with the largest percentage of long-time members (34%) while the strata with the highest number of new users were (predictably) in the banned group (18%). 

Frequency 

Sample	Strata	Less often	Several times a week	Once a day or more
Random Sample	Bans	53.64%	31.13%	15.23%
Random Sample	Removals	35%%	42.69%	22.31%
Random Sample	Unsanctioned	31.21%	46.82%	21.97%
Random Sample	Overall	37.24%	42.07%	20.69%
Ad	n/a	41.18%	41.18%	17.65%
Post	n/a	8.93%	30.36%	60.71%

Respondents tended to be relatively frequent AITA users. Banned users had the highest percentage of people who visited less than several times per week (54%) while people who took the survey via the post had the highest percentage of people who visit the sub once a day or more (61%). 

Visit Reason

Visit Reason	Overall	Post	Ads	Bans	Removals	Unsanctioned
Fun/ entertainment	66.22	89.66	70.59	56.96	64.12	72.5
Piqued interest	65.72	31.03	72.73	45.22	68.7	73.35
Consume content	28.51	53.45	41.18	22.29	26.89	32.92
Give support	27.14	20.69	18.18	22.93	31.8	25.39
Support excluded perspectives	23.65	12.07	14.71	25.48	27.65	19.44
See what’s new	23.58	46.55	35.29	16.77	26.52	24.45
Vote to influence	18.62	12.07	32.35	14.56	18.18	21
Debate	12.28	10.53	11.76	15.82	12.93	10
Connect with others	9.89	5.17	2.94	10.76	9.13	10.09
Hang out	9.45	13.79	8.82	10.13	9.09	9.4
Get support	6.62	6.9	3.03	10.76	8.37	3.13
Share content	5.03	3.45	2.94	7.05	6.92	2.5
Learn	4.76	3.45	3.03	5.73	5.34	3.8
Collaborate with others	4.61	5.17	3.12	6.33	4.62	3.75
Provoke	3.79	1.72	12.12	5.73	4.18	2.52
Gain karma	1.49	1.72	8.82	2.55	1.9	0.62
To get flair	0.27	1.72	5.88	0	0	0.63
Self promotion	0.27	0	3.03	0.63	0	0.32

We asked people why they visit AITA. This table collapses percentages of reasons that were listed as moderately important, important, and very important.

The most common visit reason is the same for each group—to have fun or for entertainment. Because a post piqued interest was also a common visit reason. However, there are some interesting variations. For example, a higher percentage of banned users and users with removals selected sharing or supporting excluded perspectives as a motivation (25% and 28% respectively) than other groups and a higher percentage of people who responded to the survey via the post selected seeing what’s new in the community as one of their top three visit reasons (47%). Users with bans and removals tended to have a higher percentage of people interested in debate (16% and 13% respectively) and people recruited via ads had the highest percentage of people interested in provoking others (12%). 

Navigation Method

We were also interested in how to how people navigate to the sub. We broke this down by people who are regular members of the community (visiting weekly or more) and casual users (people who visit less often than weekly) 

Regular Users (Weekly or more)

Navigation Method	Overall Random Sample	Banned	Removals	Unsanctioned	Post	Ads
Homepage	87.24%	69.57%	85.97%	94.2%	35.29%	100%
Directly	49.33%	56.52%	50%	46.38%	94.11%	66.67%
Frontpage	46.67%	47.83%	44.83%	47.83%	20.59%	66.67%
Link	18%	4.35%	13.79%	26.09%	8.82%	33.34%
Push notification	14.09%	17.39%	14.04%	13.05%	0%	0%
Search Engine	8.05%	8.7%	8.77%	7.25%	20.59%	0%
Reddit search	7.39%	4.35%	1.75%	13.05%	0%	0%
Direct Message	1.36%	0%	0%	1.47%	0%	0%
AI	1.34%	0%	0%	2.9%	0%	0%
Social Media	0%	0%	0%	0%	0%	0%

Casual users (Less often than weekly)

Navigation Method	Overall Random Sample	Banned	Removals	Unsanctioned	Post	Ads
Homepage	84.57%	69.04%	83.92%	93.07%	60%	75.01%
Directly	70.44%	60.94%	73.76%	72.64%	100%	67.86%
Frontpage	65.68%	55.12%	65.36%	71.43%	33.32%	78.57%
Link	61.54%	47.66%	60.19%	69.95%	47.62%	71.43%
Push notification	28.22%	25.78%	25.26%	31.98%	19.04%	10.71%
Search Engine	25.44%	24.41%	24.5%	26.75%	33.33%	17.85%
Reddit search	32.4%	35.94%	31.36%	31.41%	28.57%	21.43%
Direct Message	7.16%	8.66%	6.44%	6.99%	4.76%	0%
AI	4.71%	6.24%	4.48%	4.1%	0%	7.14%
Social Media	27.33%	21.08%	28%	30.04%	55%	29.63%

The most common navigation methods were similar between regular and casual users: high percentages of people in both groups reported visiting the sub after seeing a post on their homepage, navigating to the community directly, and after seeing a post on the front page. However, casual users reported navigating to the community in a greater number of ways. For example, in the Overall Sample, less than 15% of regular users navigated to the sub via a push notification, but 28% casual users did. No regular users navigated to the sub via a link from another social media site, whereas almost a third of casual users from the Overall Sample did.    

Motivations and participation

As part of the survey, we included questions about how often people participate in AITA in different ways, like reading, posting, commenting and upvoting/downvoting posts. Based on the responses to these questions, we created categories based on behaviors:

• Non-Active: users who report never or rarely participating in the subreddit via reading, voting, commenting or posting 
• Lurkers: users who only report reading the subreddit sometimes to very often but never or rarely comment, post or vote.
• Voter/Reactors: users whose participation in the sub is limited to reading AND upvoting/downvoting content
• Posters: All the other users, who participate in the subreddit via comment/posts.

Sample	Strata	Non-Active	Lurker	Voter/Reactor	Posters
Random Sample	Banned	15.82%	8.23%	15.19%	60.76%
Random Sample	Removals	6.06%	8.33%	26.52%	59.09%
Random Sample	Unsanctioned	2.19%	18.44%	38.75%	40.62%
Random Sample	Overall	6.47%	12.67%	29.38%	51.48%
Ad	n/a	5.88%	26.47%	41.18%	26.47%
Post	n/a	0	46.55%	32.76%	20.69%

A small percentage of users are not active in the subreddit at all. Of these users, the largest share comes, unsurprisingly, from people who have received bans in the subreddit (16%). A substantial portion of the respondents from Ads and Posts are lurkers (27% for ads, 47% for posts) or voter/reactors (41% for ads, 33% for posts), which were precisely who we were trying to reach via these recruiting approaches. Finally, the majority of our random sample (51%) comes from users who report participating in the community via comments and posts, in addition to voting and reading.

Community Climate

We also included questions about the social climate, or the vibe if you will, of the subreddit. Some of these had to do with the quality of information, some with how much people identify with the community, and some with moderator trustworthiness. We combined blocks of questions that were related to each other into three scales: Information Quality, Affective Commitment, and Moderator Trustworthiness. For example, the Information Quality scale includes questions about how trustworthy, reliable, in-depth, unbiased, and informative people perceive the information in the subreddit to be. The Affective Commitment scale includes questions about how much people identify as a member of the community, believe in the community values, find the forum personally meaningful, feel like part of a family, are emotionally attached to the community, and feel a strong sense of belonging. Finally, the Moderator Trustworthiness scale includes questions about how capable, benevolent, and having integrity people perceive the moderators to be.

Before we dive into the results, we want to give a quick note on how to read the numbers:

• When we create these scales, we take the average of the questions that go into each scale to create an index, and we set the community average to 0. We use a standardized scale where 1 unit = 1 Standard Deviation. Because of this, we are measuring each group relative to the community’s baseline. Think of the distance from -1 to +1 as the “Typical Range” of community opinions.
  • If a group’s score lands inside the typical range zone, their opinion is within the typical variation you’d expect to find in any large community.
  • If a score pushes past -1 or +1, it means that the group falls out of the typical range. They hold extreme viewpoints compared to the rest of the subreddit

Next, we show you the results for each of these scales for the banned, removals, and unsanctioned users.

No groups fall outside of the typical range of opinions about moderator trustworthiness, information quality and affective commitment. However, there is some variation across groups:

• The banned strata, represented by the pink bar with stripes, perceive much lower moderator trust, information quality and affective commitment than the overall sample. Importantly, their perception of moderator trust has a mean of (-0.81), so it sits right at the edge of the typical range. Therefore, perhaps unsurprisingly, experiencing a ban can undermine one’s trust in moderators.
• The removals strata tend to sit very closely to zero for Moderator trustworthiness, information quality and affective commitment. So having a post removed doesn’t fundamentally break one’s trust in moderators, affect how they view the quality of the information in this sub, or how attached they feel to the subreddit.  
• Finally, the unsanctioned strata perceive higher moderator trustworthiness, and a little higher information quality and affective commitment than the overall sample.

Contestation

Using these variables, we ran a couple of regression models to estimate what leads to users reporting higher comfort levels disagreeing with moderators and fellow users. 

We found that only two factors are associated with comfort disagreeing with moderators: moderator trustworthiness and amount of offensive behavior the user perceives in the subreddit. Users who trust moderators more tend to feel more comfortable expressing their disagreement with them, and users who see more offensive behavior in the subreddit feel less comfortable agreeing with moderators. None of the other factors we tried, such as status on the community, how frequently one posts, whether someone reported being harassed in the subreddit or when the user first visited the sub were significantly associated with comfort in disagreeing with moderators.

Next, we ran tests to see what is associated with comfort with disagreeing with other users.

Posting actively, and trusting mods are both associated with higher levels of comfort disagreeing with other users. The amount of offensive behavior one witnesses and being a newbie vs being a veteran in the sub are associated with less comfort in disagreeing with other users.

Rules

To understand how well people think they understand the rules of AITA, we asked them to estimate how well they would be able to explain the rules to someone else without getting into trouble. The table below reports the results across strata. 

Sample	Strata	Not at all	Somewhat	Well
Random Sample	Bans	20.25%	54.43%	25.31%
Random Sample	Removals	19.32%	63.63%	17.05%
Random Sample	Unsanctioned	21.32%	64.27%	14.42%
Random Sample	Overall	20.38%	61.94%	17.68%
Ad	n/a	41.18%	41.18%	17.65%
Post	n/a	15.52%	68.96%	15.51%

In each group, most respondents reported that they understood the rules somewhat well. However, banned users were the group with the highest percentage of respondents who reported they understand the rules well (25%) while unsanctioned users were the group with the lowest percentage of respondents who reported they understand the rules well (14%). Respondents from the ads had the highest number of respondents that reported they couldn’t explain the rules at all (41%). 

The AITA mods wanted to know whether or not respondents were reading the rules, so we asked for them. 

Sample	Strata	No	Yes
Random Sample	Bans	19.11%	80.89%
Random Sample	Removals	26.72%	73.28%
Random Sample	Unsanctioned	29.15%	70.85%
Random Sample	Overall	26.15%	73.85%
Ad	n/a	41.18%	58.82%
Post	n/a	19.64%	80.36%

Across each strata, most people report reading the rules. We also see congruous responses between the two sets of questions—banned users have the highest percentage of respondents who report having read the rules (81%) while unsanctioned users have the lowest percentage of people who report reading the rules (71%). Respondents via the ad were most likely to report not having read the rules (41%). 

AI 

While AITA is no stranger to concerns about the authenticity of posts, Generative AI and large language models (LLMs) in particular have increased these concerns. Because of the potential impact of this technology on community climate and behaviour, we wanted to ask questions about how much AI people think is on Reddit and AITA, whether or not people are using it, and if they are, how. It should be noted that r/AmItheAsshole bans AI use on the sub.

The table below shows the percentage of content users’ estimate is created by AI on Reddit and on AITA

Scale	Statistic	Overall	Banned	Removals	Unsanctioned	Post	Ads
Reddit AI use	mean	46.52%	47.25%	46.4%	46.27%	48.06%	48.71%
Reddit AI use	std	19.69%	21.92%	19.82%	18.47%	21.22%	20.88%
Reddit AI use	min	3%	9%	3%	5%	9%	15%
Reddit AI use	max	100%	100%	100%	100%	97%	100%
Sub AI use	mean	49.35%	52.11%	48.57%	48.66%	46.67%	57.94%
Sub AI use	std	23.43%	26.22%	24.18%	21.25%	24.4%	26.02%
Sub AI use	min	0%	5%	0%	1%	5%	1%
Sub AI use	max	100%	100%	100%	100%	94%	100%

Most groups (Bans, removals, unsanctioned, and ads) estimated that there is more AI-generated content on AITA than on Reddit, while respondents recruited via the post estimated a greater percentage of content was generated by AI on Reddit. Users recruited via the ads had the highest mean estimate of AI-generated content on AITA, estimating almost two thirds is AI-generated. However, most groups hovered around the 50% mark for estimates of AI generated content on Reddit and on AITA. 

Reasons why people use AI 

We also asked people why they use AI. Very few people responded that they used AI as can be seen in the table below. 

AI use	Bans	Removals	Unsanctioned	Ads	Post
Posting	1.41%	0%	0%	0%	0%
Commenting	0%	0.79%	0.68%	0%	0%
Summarizing	1.41%	0.4%	0.68%	0%	0%
Translation	0.7%	0.4%	1.7%	4.55%	0%
Research	4.23%	1.19%	2.38%	4.55%	0%
Grammar	2.82%	1.58%	3.06%	4.55%	0%
Other	2.11%	0.79%	2.72%	0%	0%
No AI	90.14%	95.26%	92.86%	86.36%	100%

Only 10% of banned users; 5% of removals, 7% of unsanctioned users, 14% of ad users, and no one recruited through the post used AI.  Of those who did, the most common uses were for research and grammar across all groups (less than 5% for each) with the ad group AI users reporting that they used it for translation (less than 5%). These findings should be interpreted with caution. We noticed a slightly higher dropoff rate for this question, meaning that many people chose not to answer it (and ad and post respondents who said they had never posted or commented on AITA were not shown this question). There could be a number of reasons for this, for example: fatigue (it was a long survey); social desirability bias (i.e., not wanting to admit to using something potentially perceived as transgressive); or, not wanting to admit violating the rules of the subreddit. 

AI Removals

We also asked people if they’d ever had anything removed for suspected AI content (note: unsanctioned users were not asked this question because they had not had any content removed). 

Sample	Strata	No	Yes
Random Sample	Banned	95.54%	4.46%
Random Sample	Removals	96.93%	3.07%
Random Sample	Overall	96.41%	3.59%
Ad	Ads	97.06%	2.94%
Post	Post	98.21%	1.79%

Most people have not had content removed for suspected AI use. As predicted, banned users had the highest percentage of people who reported AI removals (4.46%) since using AI in AITA may result in a ban. 

Reporting AI to Mods.

We also asked whether or not people reported suspected AI use to the moderators. 

Sample	Strata	No	Yes
Random Sample	Banned	84.62%	15.38%
Random Sample	Removals	82.44%	17.56%
Random Sample	Unsanctioned	89.27%	10.73%
Random Sample	Overall	85.85%	14.15%
Ad	Ads	82.35%	17.65%
Post	Post	87.72%	12.28%

Despite estimates that around half of the content on AITA is generated by AI, very few people had reported it to the moderators (between 82%-89% of all groups had not reported AI). This could be because they are unaware of how to make a report, view making reports as an endeavor that is not worthwhile, or are not confident enough in their ability to identify AI so don’t feel comfortable reporting.

The impact of AI on Community Climate 

Finally, we estimated two regression models to estimate the effect that perceiving AI content has on a user’s relationship with the subreddit, while controlling for the moderation status. Our preliminary results reveal a statistically significant AI penalty: as the perception of AI increases, a user’s sense of community attachment and their evaluation of the information quality both take hits.

The raw coefficients might appear small, but remember that Community Attachment and Information Quality are both standardized scales. To put these numbers into perspective: a user who estimates the subreddit’s content to be 50% AI-generated experiences a 0.50 standard deviation drop in community attachment and a 0.65 standard deviation drop in perceived information quality. 

As we saw in our baseline distributions, shifts of this magnitude are more than enough to pull individuals and entire groups out of the typical range of values of belonging and information quality into detachment and full skepticism of the information of the sub.

Thanks again to everyone who participated in the survey! This is a work in progress so we are open to constructive feedback and look forward to hearing what you think about these results, which you can share on the public post here.  We’ll also keep the project page updated when academic papers are published!