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arxiv: 2606.21043 · v1 · pith:FZ2H6PXL · submitted 2026-06-19 · cs.SI · cs.HC

Reducing the rate of personal insults in social media with bystander bots

Libby Hemphill , Lingyao Li , Ryan Burton , David Jurgens This is my paper

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classification cs.SI cs.HC
keywords social mediapersonal insultsdeescalationautomated repliesbystander botsRedditrandomized controlled trialonline moderation
0
0 comments X

The pith

Automated replies to insults on Reddit reduce their rate, with appreciation replies performing best.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper runs a randomized controlled trial on Reddit that posts automated replies to insulting comments using five different deescalation strategies. It finds that these replies lower the rate at which the original posters continue to post personal insults. Appreciation messages showed the strongest effect while some other strategies did not reduce insults. The work tests whether simple automated interventions can address problematic online behavior at scale. Readers care because the results point to a low-cost way to moderate social media without relying solely on human moderators or platform rules.

Core claim

In a randomized controlled trial, automated replies generated from five deescalation strategies and posted to insulting comments on Reddit reduced the rate of personal insults, with appreciation replies performing best. Not every strategy produced a reduction. The authors conclude that automated responses constitute a viable tool for addressing some problematic behaviors in online communities.

What carries the argument

Bystander bots that automatically post replies drawn from deescalation strategies to detected insulting comments.

If this is right

  • Automated replies can lower the rate of personal insults on social media platforms.
  • Appreciation-based replies outperform other tested deescalation strategies.
  • Not every deescalation message produces the same reduction in insults.
  • Automated responses offer a scalable method for addressing problematic online behaviors.
  • Such bots have both potential utility and clear limitations in practice.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same bot approach might work on other platforms if insult detection remains accurate and users do not quickly learn to ignore automated replies.
  • Combining appreciation replies with other moderation tools could produce larger effects than either alone.
  • Longer-term studies would reveal whether the reduction persists or whether users adapt their behavior around the bots.
  • The method raises questions about how users perceive and respond to automated interventions versus human ones.

Load-bearing premise

The trial design isolates the causal effect of the reply strategy itself rather than the mere presence of any reply, detection of automation, or unrelated changes in user behavior.

What would settle it

Repeating the experiment with a no-reply control group and finding no reduction in insult rates from the appreciation condition would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.21043 by David Jurgens, Libby Hemphill, Lingyao Li, Ryan Burton.

Figure 1
Figure 1. Figure 1: Overview of our experimental design We monitored comments using the Python Reddit API Wrapper (PRAW) 1 We consumed all new comments on Reddit using ’r/all’ and passed them to our insult detection model. We randomly assigned users who posted insults to either the treatment or control group the first time our system detected an insult in comments they authored. Then, when a comment was flagged as an insult b… view at source ↗
Figure 2
Figure 2. Figure 2: Visualizing the impact of intervention strategy on the karma a [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Predicted insults by count after personal insult detection [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Total comments in the week following detection [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Predicted insults by percentage after the experiment with different [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
read the original abstract

Prompted by previous research on strategies for reducing interpersonal conflict and addressing problematic behaviors in online communities, a randomized controlled trial on Reddit compared various responses for reducing the rate of personal insults users post to the site. We generated replies from five deescalation strategies and used an automated procedure for posting them as replies to insulting comments. The findings reveal that automated replies to insults can effectively reduce their rate. Appreciation performed best. Not all strategies performed well, though. We conclude that automated responses are a viable tool for addressing some problematic behaviors. We discuss their potential utility and limitations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports results from a randomized controlled trial on Reddit testing automated replies based on five deescalation strategies posted in response to insulting comments. It claims that such replies can effectively reduce the subsequent rate of personal insults posted by users, with the appreciation strategy performing best, while not all strategies were effective. The authors conclude that automated bystander responses are a viable tool for addressing problematic online behaviors.

Significance. If the causal claims hold after addressing design details, the work offers a practical, scalable demonstration of using bots to apply deescalation tactics in real social media environments. The RCT format provides direct empirical evidence rather than simulation or observational data, and the differential performance across strategies supplies a falsifiable prediction about which content works. This could inform moderation tools in online communities.

major comments (2)
  1. [Methods] Methods/Experimental Design: The manuscript provides no information on sample size, statistical power, how insults were measured or attributed across time, handling of multiple comparisons, or checks for confounds such as users detecting automation. These omissions prevent verification that the directional findings support the central claim of strategy-specific reductions.
  2. [Experimental Design] Experimental Design: No no-reply control arm is described. Without it, observed reductions cannot be attributed to the specific deescalation content (e.g., appreciation) rather than the mere presence of any automated reply or the intervention's visibility, which is load-bearing for the claim that appreciation performed best.
minor comments (1)
  1. [Abstract] Abstract: Key quantitative details such as effect sizes, confidence intervals, or exact p-values for the strategy comparisons are absent, which would strengthen the directional claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments identify key areas where additional detail and clarification will improve the work. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Methods] Methods/Experimental Design: The manuscript provides no information on sample size, statistical power, how insults were measured or attributed across time, handling of multiple comparisons, or checks for confounds such as users detecting automation. These omissions prevent verification that the directional findings support the central claim of strategy-specific reductions.

    Authors: We agree that these methodological details are necessary for readers to evaluate the study. The revised manuscript will expand the Methods section to report the total sample size, a priori power analysis, the precise operationalization and measurement of personal insults (including the classifier or coding scheme and the temporal window for attribution to the same user), the procedure for handling multiple comparisons, and any post-experiment checks for users detecting automated replies (such as analysis of reply timing or user comments). revision: yes

  2. Referee: [Experimental Design] Experimental Design: No no-reply control arm is described. Without it, observed reductions cannot be attributed to the specific deescalation content (e.g., appreciation) rather than the mere presence of any automated reply or the intervention's visibility, which is load-bearing for the claim that appreciation performed best.

    Authors: The design compared five active deescalation strategies to identify relative differences in effectiveness, consistent with the goal of determining which bystander content is most useful. We acknowledge that the lack of a no-reply control prevents strong claims about absolute reduction versus receiving any reply. The revision will add an explicit discussion of this design decision in the Methods and Limitations sections, reframing the primary conclusions around comparative strategy performance while noting the limitation for absolute-effect interpretations. revision: partial

Circularity Check

0 steps flagged

Empirical RCT reports observed outcomes with no derivation chain

full rationale

The paper is a randomized controlled trial that posts automated replies according to five strategies and measures subsequent insult rates. No equations, fitted parameters, or first-principles derivations are present; results are reported as direct experimental comparisons. No step reduces a claimed prediction to its own inputs by construction, and the design does not rely on self-citation for its central empirical claim.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The study is an empirical experiment that relies on standard assumptions of randomized trials rather than new mathematical axioms or invented entities.

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discussion (0)

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