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Queueing for Civility: User Perspectives on Regulating Emotions in Online Conversations

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Delaying flagged comments about 47 seconds cuts anger and hate speech by up to 15 percent in a replay of 15,000 Reddit threads.

desk verdict The 15% anger-reduction claim is an artifact of the simulation's own rule; the design idea is plausible but unproven. read the letter →

arxiv 2507.11477 v1 pith:OCZYUGQN submitted 2025-05-05 cs.CY cs.HC

Akriti Verma , Shama Islam , Valeh Moghaddam , Adnan Anwar This is my paper · ORCID
classification cs.CYcs.HC
keywords commentqueuingemotionregulationself-reflectiononlinetoxicityhatespeechreductionsocialmediamoderationdigitalemotionalcontagion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes a comment queuing mechanism for online discussions: a comment that would push a conversation's 'emotion board' past a threshold is held briefly before publication, giving the author a pause for self-reflection and letting the conversation cool down. Analyzing 15,000 political Reddit interactions replayed with and without the queue, the authors report that the mechanism reduces the spread of anger and hate speech by up to 15 percent while holding only about 4 percent of comments for an average of 47 seconds. A 20-person survey adds that 93.3 percent of participants believed the mechanism would calm discussions and 83 percent thought it would reduce impulsive comments, but perceptions depended on the user's typical emotional state: calm users found the delay helpful, frustrated users expected frustration. The paper's contribution is a proactive, pre-publication form of digital emotion regulation rather than another post-hoc moderation tool.

What carries the argument

The load-bearing object is the 'emotion board' of the root node: the aggregate $E_m(R) = \sum_i E_m(V_i)$ of per-comment influence scores $E_m(V_i) = f(A_i)$, where $f(A_i)$ combines the comment's reply count, distance from the root, PageRank, and NRC emotion intensity. The mechanism publishes a comment only if it would not push any emotion past a per-emotion threshold (e.g., anger > 50%, fear > 60%); otherwise the comment is queued and re-evaluated on every new comment. Thresholds are dynamic—adjusted by conversation size, current emotion distribution, engagement level, and a sliding window of the most recent 100 comments—so moderation tracks the conversation's evolving tone. The queue is what converts the emotion board from a measurement into an intervention: it creates the reflective pause and changes which comments ever enter the graph.

What would settle it

Run a live randomized trial on a social platform: flag comments with the same anger/fear thresholds, delay a random half for about 47 seconds, and compare conversation-level emotion scores and hate-speech reports; if delayed comments do not lower anger by roughly 15 percent, or if user frustration and disengagement offset the gain, the claim fails.

Watch

Extended reading notes

Core claim

The paper claims that a pre-publication queue on emotionally charged comments measurably calms online conversations. Replaying 15,000 political Reddit interactions as directed acyclic graphs, it compares the root node's emotion board—an aggregate of per-comment influence scores built from reply counts, distance from the root, PageRank, and NRC emotion intensities—with and without the queue. The queued condition reduces average anger by about 15 percent and lowers fear while increasing joy and love, achieved not by deleting angry comments but by slowing their entry so no single emotion dominates. The operating cost is small: 4 percent of comments are held, on average for 47 seconds, and even in threads with up to 7,000 comments roughly 1,000 comments are held. A 20-user survey adds that 93.3 percent see the mechanism as calming and 83 percent as curbing impulsiveness, but this perception is split along the user's own emotional state.

Load-bearing premise

The 15 percent reduction rests on treating the emotion-board threshold rule as a faithful model of real conversation, without accounting for how users react to being delayed.

Editorial extensions

If this is right

  • Deployed on a platform, the queue would reduce the anger contribution to a conversation's emotional tone by roughly 15 percent without deleting or editing any comment.
  • Only about 4 percent of comments would be held, each for under a minute on average, so the mechanism preserves the natural pace of most exchanges.
  • Users' baseline emotional state predicts acceptance: calm and happy users find the delay helpful, frustrated users expect it to frustrate them, so rollouts should anticipate emotion-dependent pushback.
  • Dynamic thresholds and the 100-comment sliding window let the mechanism follow a conversation's emotional trajectory in real time rather than imposing a fixed toxicity rule.
  • Survey evidence indicates broad stated willingness—93.3 percent support calming conversations and 83 percent expect fewer impulsive comments—suggesting user appetite for proactive emotion-regulation features.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the simulation simply excludes comments that score above threshold rather than letting authors react, the true in-the-wild effect may differ; the survey's frustrated users are precisely the population most likely to contribute flagged comments, so a real deployment could trade reduced toxicity for higher user frustration and disengagement.
  • The same queuing logic could be tested in adjacent real-time media—email, chat, or messaging—where a brief cooling-off hold on emotionally hot drafts might reduce regret and interpersonal conflict; this is an extension the paper does not explore.
  • The calibration is limited to English-language political Reddit threads collected from August 2023 to August 2024; porting the thresholds to other languages, platforms, or non-political topics would require re-estimating emotion scores and re-tuning the dynamic thresholds.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a comment queuing mechanism that delays publication of emotionally charged comments in online discussions, with the goal of promoting self-reflection and reducing emotional contagion. The authors evaluate the idea with a simulation on 15,000 Reddit interactions, claiming an average reduction of about 15% in anger and fear with only 4% of comments delayed by roughly 47 seconds, and with a user survey (N=20) reporting that 93.3% of participants believed the mechanism would calm discussions. The paper frames the contribution as a real-time, proactive complement to post-hoc content moderation.

Significance. The underlying idea—intervening before a comment is published by introducing a reflective delay—is worth exploring in HCI and social computing. If the 15% reduction were a genuine empirical finding, it would be practically relevant. However, the central quantitative claim is not supported as presented: the simulation's with-queue condition withholds the very comments that contribute most to the measured emotion, making the reduction a mathematical artifact of the evaluation rather than an observed behavioral effect. The survey is too small and its reported percentages are internally inconsistent, further undermining the evidence. The paper does not ship reproducible code and leaves the core influence function unspecified. The concept has merit, but this manuscript does not currently provide a valid demonstration of the mechanism's efficacy.

major comments (4)
  1. [§4.1 and §3.4–3.5] The headline claim of a 15% reduction in anger is circular. In the with-queue condition, comments that exceed the emotion thresholds are withheld from the conversation graph before the root node's emotion board is recomputed. Since the emotion board is the aggregate of per-comment influence scores Em(Vi) = f(Ai) (Eq. 1), excluding the highest-emotion comments guarantees a lower aggregate anger/fear score. No downstream user response to the delay is modeled; the paper itself states the queue 'does not eliminate angry comments, but moderates their impact on the conversation's emotional tone' (§4.1), which reduces 'impact' to simple absence from the aggregate. To support the causal claim about reducing spread, the simulation needs to model how delayed publication changes subsequent replies, or at minimum compare against a control that withholds an equivalent number of random comments.
  2. [§4.2.1] The survey statistics are internally inconsistent with the reported N=20. Percentages such as 93.3%, 41.7%, 66.7%, 33.3%, 21.4%, and 42.9% imply denominators of 15, 12, 6, 3, 14, and 7 respectively, none of which match N=20 or the described subgroup counts. For example, 93.3% of 20 people is 18.66 individuals, which is impossible. These inconsistencies apply to the descriptive percentages and to the chi-square and Cramér's V results, which require observed and expected cell counts not provided in the paper. The authors must supply the actual response contingency tables and either use exact tests appropriate for small N or reduce the statistical claims accordingly.
  3. [§3.4, Eq. (1)] The influence score Em(Vi) = f(Ai) is never given a concrete functional form. The text mentions number of replies, distance from root, PageRank, and emotion intensity, but does not specify how these are combined into f. This makes the emotion board and hence the 15% reduction figure non-reproducible. Please specify f explicitly (e.g., a weighted sum with the stated factors), and ideally release code or a detailed parameter listing.
  4. [§3.5 and §4.1] The queuing simulation depends on several unspecified or coarsely described parameters: the initial emotion thresholds (e.g., 'Anger > 50%, Fear > 60%'), the dynamic threshold adjustment rules, the sliding window size (only 'last 100 comments' is mentioned), and the criterion for 'when the conversation yields a more balanced emotional tone' that releases queued comments. These are free parameters, and the reported 4% delay rate and 15% reduction may be sensitive to them. A sensitivity analysis or a clearly fixed parameter set with justification is necessary to assess the robustness of the headline numbers.
minor comments (6)
  1. [Abstract and §4.2.1] The abstract reports 93.3% for participants believing the mechanism calms discussions, while Section 4.2.1 also reports 83% for balanced emotional tone and 93.3% for reducing trolling. Please ensure all survey percentages are consistent and traceable to the raw responses.
  2. [§3.3 and §4.1] The term '15,000 user interactions' is used interchangeably with '15,000 instances of user posts and interactions'; clarify whether this count refers to comments, replies, or both, and how it relates to the 65 threads.
  3. [§3.5, Fig. 2] The caption 'Number of comments < 2000' is ambiguous; it presumably means thread size categories. Please clarify the x- and y-axis labels of the histograms and state the units (seconds and frequency).
  4. [§5, Limitations] The limitations section acknowledges the simulated setting and small N but does not acknowledge that the evaluation procedure removes the to-be-measured high-emotion comments. Add an explicit statement of this circularity and its implications for interpreting the 15% figure.
  5. [References] Reference [Edwards 2005] lists 'RA Fischer' but the correct spelling is 'R. A. Fisher'; also, the reference format for [Verma et al. 2023] appears in the bibliography as an arXiv preprint, which should be clearly marked as prior work by the same authors.
  6. [Throughout] There are several minor grammatical and typographical errors (e.g., 'these subreddit are known' in §3.2, 'Emojinal library' without a citation link). A careful proofreading pass is needed.

Circularity Check

1 steps flagged · score 8.0 of 10

The 15% anger-reduction result is an artifact of the evaluation: comments are queued when they exceed emotion thresholds, and the measured emotion board is the same aggregate from which those comments are withheld, making the reduction a consequence of the mechanism's rules rather than a behavioral effect.

  1. self definitional [Section 3.5 (queuing rule) and Section 4.1 (claimed 15% reduction), with Eq. (1)]
    "Upon submission of a comment, the proposed framework evaluates its potential emotional impact on the conversation’s emotion board. The emotion board aggregates the emotional influence of all comments in the discussion, with specific thresholds set for each emotion (e.g., Anger > 50%, Fear > 60%). For instance, if a comment elicits an anger level exceeding 50% or a fear level surpassing 60%, it is identified as potentially disruptive. Instead of being published instantaneously, these flagged comments are placed into a queue for periodic re-assessment."

    Equation (1) defines the emotion board as the sum of per-comment influences Em(V_i) = f(A_i), i.e., the aggregate of comments' emotion intensities. The queueing rule in the quoted passage uses that same board and thresholds to hold any comment with anger above 50%. Section 4.1's 'with queue' condition then measures the board after such comments have been withheld from the conversation, so their anger contributions are absent from the aggregate during the window being scored. The average ~15% anger reduction is therefore a logical consequence of removing the very comments selected by the anger threshold from the quantity used to measure anger, not a behavioral effect of delay on other users.

full rationale

The paper's headline quantitative claim is that the queue 'could reduce the spread of hate speech and anger by up to 15%' (Abstract) and that 'the queue mechanism achieved an average reduction of approximately 15% in anger' (Section 4.1). The evaluation compares a no-queue condition with a queue condition in which comments are held whenever they exceed emotion thresholds. The outcome measure is the root node's 'emotion board', defined in Eq. (1) as the aggregate of per-comment emotion influences Em(V_i) = f(A_i). Because the queue condition withholds comments with high anger from exactly that aggregate, the measured anger reduction is guaranteed by the evaluation procedure. The paper even acknowledges that the queue does not eliminate angry comments but 'moderates their impact on the conversation's emotional tone' — and that 'impact' is the same aggregate from which the held comments are missing. This is not an empirical finding about how delay changes subsequent user behavior; the simulation does not generate replies contingent on delay or model emotional contagion after a pause. The survey component reports participants' anticipated reactions, which is independent and not circular, but it cannot validate the 15% figure. The hold-duration statistics (4% delayed, ~47 seconds) are descriptive and not themselves circular. There is one self-citation to the authors' prior work, but it is not load-bearing for the circularity finding. Overall, the central computational efficacy claim reduces by construction, warranting a score of 8.

Assumptions & free parameters 4 free parameters · 3 assumptions · 1 invented entities

The central 15% reduction claim rests on an underspecified influence function f(A_i), author-chosen emotion thresholds, and a simulation that withholds comments above those thresholds, so the outcome is largely predetermined. The survey adds only self-reported perceptions from 20 self-selected participants.

free parameters (4)
  • Emotion thresholds for queuing = Anger > 50%, Fear > 60%
    Chosen by the authors in Section 3.5; the simulated 15% reduction in anger depends directly on these values.
  • Sliding window size = 100 most recent comments
    The mechanism only considers the last 100 comments, which affects threshold adjustments and the measured emotion board.
  • Dynamic threshold adjustment parameters = unspecified
    Section 3.5 says thresholds are adjusted based on conversation activity, emotion distribution, and engagement, but no formulas or fitted values are given.
  • Influence score function f(A_i) = undefined
    Equation (1) defines each comment's emotional influence as f(A_i), but the function is never defined; this scoring drives both the queuing decisions and the reported reduction.
assumptions (3)
  • domain assumption NRC Word-Emotion Association Lexicon provides valid emotion intensity scores for Reddit comments.
    Used in Section 3.3 to assign emotion intensities; no validation on Reddit text.
  • domain assumption The DAG-based influence model (replies, distance from root, PageRank, emotion intensity) accurately measures a comment's emotional impact on the root node.
    Section 3.4 defines influence but provides no evidence that this aggregation matches real conversational impact.
  • domain assumption A short delay before posting induces self-reflection and reduces impulsive or toxic behavior.
    Section 3.1 relies on behavioral science literature without testing the causal link in this setting.
invented entities (1)
  • Emotion board of the root node
    purpose: Aggregates emotional influence of all comments to decide queuing and to measure conversation tone.
    The emotion board is a new construct defined in Section 3.5 and used as the outcome metric; it is not externally validated.

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Cite this review

Pith. "Pith review of Queueing for Civility: User Perspectives on Regulating Emotions in Online Conversations." pith.science (2026). https://pith.science/paper/OCZYUGQN

@misc{pith2026250711477,
  author       = {Pith},
  title        = {Pith review of: Queueing for Civility: User Perspectives on Regulating Emotions in Online Conversations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OCZYUGQN}},
  note         = {Machine review of arXiv:2507.11477}
}
read the original abstract

Online conversations are often interrupted by trolling, which causes emotional distress and conflict among users. Previous research has focused on moderating harmful content after it has been posted, but ways to manage emotions in real-time remain unexplored. This study suggests a comment queuing mechanism that delays comment publishing, encourages self-reflection, and reduces the impact of impulsive and toxic comments. To assess the efficacy of this approach, a mixed-method research design is used. An analysis of 15,000 user interactions on Reddit showed that this approach could reduce the spread of hate speech and anger by up to 15%, with only 4% of comments being delayed for about 47 seconds on average. We also surveyed users for feedback on the mechanism. The results showed that 93. 3\% of the participants thought that the queuing mechanism could help calm the discussions and showed interest in seeing it used on social media platforms. Furthermore, 83% believed it would reduce impulsive comments and balance the emotional tone in conversations. We found a strong link between users' typical emotional states while using social media and their perceptions of the delay, with calm users finding the mechanism helpful and frustrated users anticipating frustration.

Figures

Figures reproduced from arXiv: 2507.11477 by the authors.

Figure 1
Figure 1. Proposed Comment Queuing to encourage Self-reflection [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Average Distribution of Held Durations for Comments when using the proposed queue approach. (a) [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. The violin plot presents the distribution of emotions in the emotion board of the root node for online [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Average emotion levels in the conversation when using the queue [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Perceived helpfulness of the delay. (a) Chi-square results. (b) Pairwise statistics. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Effect of Emotional State on users’ perception of how the delay in adding comments impacts the [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

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