Reactive Users vs. Social Recommender Systems: Managing Opinion Drifts with Adaptive Policies

Atefeh Mollabagher; Parinaz Naghizadeh

REVIEW 3 major objections 4 minor 30 references

Reactive Users vs. Social Recommender Systems: Managing Opinion Drifts with Adaptive Policies

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that a user who lowers click probability after large opinion shifts can limit recommender-induced opinion drift, and can achieve higher expected utility than a fixed-clicking user when preserving opinion matters more than e

desk verdict The abstract promises a useful and checkable result, but the corrupted full text means I can't yet endorse it; the modeling premise deserves referee scrutiny once a readable version exists. read the letter →

arxiv 2508.13473 v2 pith:R6NQAC4I submitted 2025-08-19 cs.GT

Atefeh Mollabagher , Parinaz Naghizadeh This is my paper

classification cs.GT MSC 91D30

keywords opiniondynamicsrecommendersystemsreactivepoliciesdriftuserutilitysocialnetworkinfluencestochasticconsumptionengagement

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

Recommendation systems pull people's opinions as a side effect of maximizing engagement. This paper asks whether a user who notices that pull can resist it simply by changing how often they click on recommended content. It models a user whose opinion evolves under the combined influence of a social network and a recommender, and compares a passive policy with a fixed click probability to a reactive policy where the click probability drops after large opinion shifts. The paper derives closed-form expressions for expected opinion and expected utility under both policies, and finds that the reactive policy limits recommendation-induced drift and can give higher expected utility than the passive policy when the user cares more about preserving their opinion than about engagement. Why this matters: it suggests individual engagement choices, not just platform-side algorithmic fixes, are a workable defense against opinion drift.

What carries the argument

The reactive consumption policy is the central object: at each step the user clicks on recommended content with a probability that adaptively decreases after large opinion drifts. This policy is the mechanism that blocks drift, because the recommender's influence on opinion is scaled by the click probability, so lowering engagement at moments of drift removes the force that was pushing the opinion away. The analysis tracks the first two moments of the opinion process to derive expected opinion and expected utility, and the utility trade-off between engagement and opinion preservation determines whether the reactive policy beats the fixed baseline.

What would settle it

A matched comparison of users under fixed versus reactive click policies would settle it: if the reactive group's opinion drift is not smaller, or its expected utility is not higher in the drift-averse parameter regime, the central claim fails. The sharpest control is to let the recommender adapt its content to the user's engagement; the paper's predictions assume it does not, so any reversal in that setting would locate the boundary of the claim.

Watch

Extended reading notes

Core claim

The central claim is that a user can counteract the recommender's influence on their opinion by making their click probability a decreasing function of recent opinion drift. Because the recommender's pull is modeled as proportional to how often the user consumes recommended content, reducing consumption after a large drift directly shrinks the recommender's contribution to the next opinion update. The paper derives the expected opinion trajectory and expected user utility under both the fixed and the reactive policy, and shows the reactive policy keeps the expected opinion closer to the user's social-network-driven position. It further shows that in the regime where the user's utility weight

Load-bearing premise

The drift-prevention result relies on the recommender's influence being proportional to the user's click rate and on the recommender not changing its recommendations when the user clicks less.

Editorial extensions

If this is right

A user does not need to leave the platform or demand algorithmic change; reducing click frequency after drift is sufficient to bound recommendation-induced opinion change.
For users whose utility puts a large weight on opinion preservation, the reactive policy can yield higher expected utility than fixed clicking, so the best user strategy depends on how much the user values staying true to their original opinion.
The expected opinion under the reactive policy is closer to the opinion that would result from the social network alone, meaning the recommender's bias is dampened rather than eliminated.
The closed-form expressions for expected opinion and utility provide a way to tune the reactivity threshold: how large a drift must be before the user starts backing off.

Reading between the lines

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

If the recommender reacts to reduced engagement by changing what it recommends, the drift-prevention guarantee may not survive; the paper models content selection as exogenous, so a two-sided model where the recommender adapts is a natural extension.
The policy comparison points to a measurable real-world signature: users who intermittently disengage after opinion shifts should show less drift on attitude scales, which could be tested with panel data from social media feeds.
If enough users adopt reactive policies, the aggregate demand signal to the recommender changes and may alter the content distribution; this system-level feedback is not modeled in the paper.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First thing to know: the submission we can actually read is just the abstract. The full text is a mojibake mess of replacement characters, so nothing in the derivations or simulations is auditable. On the strength of the abstract alone, the paper is worth a minute but not a citation yet.

What's new: the reactive consumption policy—user lowers click probability when their opinion drifts—paired with an analytical expected-utility comparison against a fixed policy. That specific comparison does stand apart from the prior literature the abstract cites. The claims are also carefully conditional: 'can help,' 'when the user prioritizes opinion preservation.' That is honest framing.

What the paper does well, on the abstract's evidence: it picks a tractable single-user model and asks a clear question about whether user-level interventions can resist recommender-induced drift. That is a legitimate contribution to the opinion-dynamics/recommender-systems subfield.

Where I'd push back: the drift-prevention result is partly built into the policy—if you define 'reactive' as 'consume less after drift,' then you will get less recommender influence by construction. The real load-bearing result is the expected-utility comparison, and in the abstract we cannot see the parameter regime that makes it work. The weight on opinion preservation is left unspecified.

The stress-test concern about the recommender staying exogenous is real but not fatal. It is a modeling premise, not an internal inconsistency: the user is the only adaptive agent, and the recommender's content distribution does not respond to reduced clicks. If the recommender adapts to re-engage the user, the guarantee is unprotected. That is worth stating explicitly in the paper, and it should appear as a limitation section, not buried.

The other soft spot is purely practical: until there is a clean version, no referee can check the math. That is an editorial problem, not an intellectual one.

Who is this for: researchers working on intervention design in opinion dynamics and on filter bubbles from a user-side perspective. It would be a reasonable reading-group paper if a readable version is obtained.

My recommendation: send it to peer review after asking the authors for a readable PDF. The claims are checkable, the question is relevant, and the conditional framing suggests the authors know the scope. My own verdict right now is unverified, not negative.

Referee Report

3 major / 4 minor

Summary. This paper studies a single user whose opinion evolves under the influence of a social network and a recommender system. Two content-consumption policies are compared: a passive policy with fixed click probability on recommendations, and a reactive policy in which the click probability decreases after a large opinion drift. The authors claim to derive closed-form expressions for the expected opinion and user utility under both policies, and conclude that the reactive policy can prevent recommendation-induced drift and can yield higher expected utility when the user's utility sufficiently prioritizes opinion preservation. Numerical simulations are reported as validation. As submitted, the full text is substantially corrupted, which prevents independent verification of the derivations.

Significance. The question is timely: user-side countermeasures to recommendation-induced opinion drift are understudied relative to platform-side interventions. The fixed-vs-reactive policy distinction is simple and analytically tractable, and the expected-utility comparison offers a concrete tradeoff between engagement and opinion stability. The authors use appropriately cautious 'can help' / 'can outperform' language. If the derivations are correct, the paper provides a useful baseline model for user-level adaptation. However, the headline drift-prevention result is largely built into the modeling assumption that recommendation influence is mediated by and scales with the click rate, so the independent content is mainly the utility comparison. The manuscript does not contain machine-checked proofs or code, and the current text makes it impossible to verify the analytic claims.

major comments (3)

[Section 2 (model setup)] The protective effect of the reactive policy depends on the recommender's content distribution being exogenous and independent of the user's click history. For the real systems the paper invokes, a recommender that optimizes engagement will typically respond to reduced clicks with different, possibly more extreme or confirmation-biased recommendations. The paper does not state this limitation, nor does it show how the derivations change when the recommendation distribution depends on past consumption. Because the mechanism of drift prevention is 'lower consumption -> lower influence', an adaptive recommender can break the mechanism. Please make this assumption explicit and discuss robustness; ideally add a simple extension in which the recommender's content distribution responds to engagement.
[Section 3 (analytical derivations)] The analytical claims could not be checked: the submitted full text contains widespread character corruption, and the displayed equations are not fully legible. There are no clear theorem or proposition statements with hypotheses and proof steps. The assertion that the authors 'analytically derive the expected opinion and user utility' is therefore currently unsupported in the submitted file. A publishable version must contain clean equations, numbered assumptions, and verifiable derivations or proofs.
[Abstract and Section 3] The drift-prevention result is close to tautological: the reactive policy is defined as reducing the click probability after large drifts, and the model assumes recommendation influence scales with the click rate, so reduced drift follows largely by construction. The independent content is the expected-utility comparison. The paper should reframe the contribution accordingly and prove the utility dominance under explicit inequalities on the utility weight, threshold, and step size, rather than leaving it as a simulation-supported 'can'. This would also clarify which parameter regime is the intended contribution.

minor comments (4)

[Abstract and Section 2] The phrase 'probability of content consumption adaptively decreases' should specify whether the decrease is permanent or resets after the opinion returns to normal; the threshold and step-size parameters need precise definitions.
[Utility definition] The utility function's horizon (finite T versus infinite discounted) is not clear; expected utility is ambiguous without it. Please state the horizon and whether the expectation is over the stochastic content stream or also over user actions.
[Model notation] The roles of the social network and the recommender should be distinguished explicitly in the equations; for example, define all influence weights and the recommendation distribution in one dedicated notation block.
[Numerical validation] The simulation section is not fully legible. Ensure that all plots have axis labels, parameter values are listed, and the simulation setup is described so that the numerical validation can be reproduced.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the drift-prevention and expected-utility results are derived from explicitly stated model assumptions and a defined behavioral rule, not from self-referential definitions or fitted predictions.

full rationale

The paper specifies two distinct objects: a consumption policy (passive p_t fixed; reactive p_t decreasing after large opinion drifts) and an opinion-dynamics model in which recommendation influence is mediated by consumption. The conclusion that the reactive policy reduces recommendation-induced drift is a mathematical consequence of these definitions, but it is not identical to the input: the policy is not defined as 'drift is prevented'; it is defined as a consumption rule that responds to past drift, and the expected-opinion calculation must still be carried out. The expected-utility comparison has independent content, since it involves a separate opinion-preservation weight and a trade-off between reduced engagement and reduced drift; the paper does not fit any parameter to the outcome it then announces. There are no load-bearing self-citations, no imported uniqueness theorems, and no fitted values renamed as predictions. The skeptical concern that a real recommender would adapt to reduced engagement is a modeling-validity caveat, not a circularity, and is outside the scope of this pass. The numerical simulations validate the analytic formulas rather than replacing them. The derivation chain is therefore self-contained.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on a small set of domain assumptions (exogenous recommender, monotone influence of click rate, user drift detection) and on model design parameters (utility weights, drift threshold, adaptation step size). No new physical or mathematical entities are introduced; the reactive policy is a strategy, not an entity. The parameters are set by the modeler rather than fitted to data, which is honest to the modeling exercise, but it limits the scope of the utility comparison to the chosen regime.

free parameters (4)

Utility weight on opinion preservation vs engagement
The utility comparison depends on the user 'prioritizing opinion preservation'; the relative weight of drift avoidance versus engagement is a model design choice not specified in the abstract.
Opinion drift threshold triggering adaptation
The reactive policy activates on 'large opinion drifts'; the threshold magnitude is a hand-chosen design parameter that determines how often the policy engages.
Click probability adjustment amount (step size)
The reactive policy 'adaptively decreases' consumption probability; the size and functional form of the decrease are model parameters.
Relative influence strengths of recommender and social network
Expected opinion depends on the relative strengths of the two influence channels; parameter values used in simulations are not stated in the abstract.

assumptions (4)

domain assumption Opinion update depends monotonically on the rate of clicked recommendations.
Expected-opinion derivations require a functional link between click probability and opinion change; the abstract states only that the user 'is influenced by both a social network and the recommender' without specifying the update rule.
domain assumption Recommender content distribution is exogenous and does not adapt to the user's click history.
The reactive policy's drift prevention presumes the recommender does not counter-adapt to reduced engagement; real recommenders learn from user behavior, so this is load-bearing.
domain assumption The user can detect opinion drift and commit to the prescribed adjustment policy.
Reactive behavior requires drift monitoring and self-control; this behavioral capability is assumed rather than established.
standard math Standard stochastic-process techniques for expected values are applied to the opinion process.
Analytical derivations of expected opinion and expected utility rely on standard expectation and martingale arguments, claimed in the paper but not verifiable from the corrupted text.

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Cite this review

Pith. "Pith review of Reactive Users vs. Social Recommender Systems: Managing Opinion Drifts with Adaptive Policies." pith.science (2026). https://pith.science/paper/R6NQAC4I

@misc{pith2026250813473,
  author       = {Pith},
  title        = {Pith review of: Reactive Users vs. Social Recommender Systems: Managing Opinion Drifts with Adaptive Policies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R6NQAC4I}},
  note         = {Machine review of arXiv:2508.13473}
}

read the original abstract

Recommendation systems are used in a range of platforms to maximize user engagement through personalization, promotion of popular content, and the use of information from social networks. It has been found that such recommendations may shape users' opinions over time. In this paper, we ask whether reactive users, who are cognizant of the influence of the content they consume, can limit such changes by adaptively adjusting their content consumption choices. To this end, we study users' opinion dynamics under two stochastic content consumption policies: a passive policy, where the probability of clicking on recommended content is fixed, and a reactive policy, where the probability of content consumption adaptively decreases following large opinion drifts. We analytically derive the expected opinion and user utility under these policies when a user is influenced by both a social network and the recommender. We show that the adaptive policy can help users prevent opinion drifts induced by recommendations and that when a user prioritizes opinion preservation, the expected utility of the adaptive policy can outperform the fixed policy. We validate our theoretical findings through numerical simulations. These findings help better understand how user-level strategies can challenge the biases induced by recommendation systems.

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Reference graph

Works this paper leans on

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Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

R. M. Ursu, ``The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions,'' Marketing Science, vol. 37, no. 4, pp. 530--552, 2018

work page 2018

[2] [2]

Allcott, L

H. Allcott, L. Braghieri, S. Eichmeyer, and M. Gentzkow, ``The welfare effects of social media,'' American economic review, vol. 110, no. 3, pp. 629--676, 2020

work page 2020

[3] [3]

Levy, ``Social media, news consumption, and polarization: Evidence from a field experiment,'' American economic review, vol

R. Levy, ``Social media, news consumption, and polarization: Evidence from a field experiment,'' American economic review, vol. 111, no. 3, pp. 831--870, 2021

work page 2021

[4] [4]

G. J. Martin and A. Yurukoglu, ``Bias in cable news: Persuasion and polarization,'' American Economic Review, vol. 107, no. 9, pp. 2565--2599, 2017

work page 2017

[5] [5]

L. T. L. Terren and R. B.-B. R. Borge-Bravo, ``Echo chambers on social media: A systematic review of the literature,'' Review of Communication Research, vol. 9, 2021

work page 2021

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T. Di Noia, N. Tintarev, P. Fatourou, and M. Schedl, ``Recommender systems under european ai regulations,'' Communications of the ACM, vol. 65, no. 4, pp. 69--73, 2022

work page 2022

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W. S. Rossi, J. W. Polderman, and P. Frasca, ``The closed loop between opinion formation and personalized recommendations,'' IEEE Transactions on Control of Network Systems, vol. 9, no. 3, pp. 1092--1103, 2021

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