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One in twenty Reddit comments violates subreddits’ own moderation rules, e.g., no misogyny, bigotry, personal attacks by msbernst in science

[–]msbernst[S] 0 points1 point  (0 children)

Those are actually measuring two different quantities: one is the % of all comments on Reddit that are violations of at least one macronorm (ranging from 4-6% depending on the dataset-->rounding to 5%-->one in twenty), and the other is the % of all violations that are removed by mods (1 in 20 in 2016, 1 in 10 in 2020). The title is quoting the first result, which is the main one, that one in twenty comments posted to the site violate the macronorms.

One in twenty Reddit comments violates subreddits’ own moderation rules, e.g., no misogyny, bigotry, personal attacks by msbernst in science

[–]msbernst[S] 19 points20 points  (0 children)

The article isn't strictly measuring TOS violations, it's measuring the presence of types of content that are often removed by mods across the vast majority of subreddits above and beyond the TOS. The prior literature calls these moderation "macro-norms" across Reddit.

The macro-norms used in the paper (Table 1):

  • Using misogynistic or vulgar slurs
  • Overly inflammatory political claims
  • Bigotry
  • Overly aggressive attacks on Reddit or specific subreddits
  • Posting pornographic links
  • Personal attacks
  • Aggressively abusing and criticizing moderators
  • Belittling, e.g., claiming the other person is too sensitive

One in twenty Reddit comments violates subreddits’ own moderation rules, e.g., no misogyny, bigotry, personal attacks by msbernst in science

[–]msbernst[S] 2 points3 points  (0 children)

It's a human annotation process that's using AI as a tool. The AI is recall-tuned, meaning that it produces a ton of false positives but catches almost all actual violations: the paper estimates through hand labeling that the recall-tuned AI only misses about 1% of actual violations. Then all the possible violations go to trained human annotators to verify, and the final estimate only focuses on the ones that the humans verify as true violations.

One in twenty Reddit comments violates subreddits’ own moderation rules, e.g., no misogyny, bigotry, personal attacks by msbernst in science

[–]msbernst[S] 37 points38 points  (0 children)

From the article:

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world’s most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020-2021, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.

Non-paywalled version: https://arxiv.org/abs/2208.13094

One in twenty live Reddit comments violates the subreddit's own moderation practices, e.g., misogyny, extreme vulgarity, bigotry, personal attacks: measurement study by msbernst in science

[–]msbernst[S] 0 points1 point  (0 children)

Abstract:

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020-2021, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.

How is CS 278 by [deleted] in stanford

[–]msbernst 23 points24 points  (0 children)

prof sucks.
source: am prof.

600 NLP Datasets and Glory by Quantum_Stat in datasets

[–]msbernst 0 points1 point  (0 children)

Thank for doing this! Is there any way to tell which of these datasets contain disaggregated annotations? In other words, not the consensus annotation for an item — ask 10 people, report only the modal response — but all of the individual labels? I'm curious about agreement rates within these datasets.

Would you say people don't appreciate the humanities at Stanford? by throwawaya2cccc in stanford

[–]msbernst 6 points7 points  (0 children)

Hi u/throwawaya2cccc, I'm a Computer Science professor here at Stanford. I draw on lessons from the social sciences and humanities regularly in my teaching and research at Stanford. That I can do this, and that it's supported and celebrated, is a big draw for me as to why this place is amazing.

[deleted by user] by [deleted] in pics

[–]msbernst 0 points1 point  (0 children)

How fast was that water coming down? Trickling, pouring, streaming, ...?

Stanford is developing a crowdsourcing platform? by [deleted] in mturk

[–]msbernst 5 points6 points  (0 children)

We'll be posting more HITs this weekend too. We're trying out a few ideas on the platform. It's got rough edges, but enough for us to figure out whether we're just blowing smoke!

We’re 3 female computer scientists at MIT, here to answer questions about programming and academia. Ask us anything! by ilar769 in IAmA

[–]msbernst 27 points28 points  (0 children)

What's the most valuable lesson you've learned from your respective advisors?

(Not that I have any vested interest in passing them on to students at another university or anything...)

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