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Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Contextual factors such as being at home, sleepiness, and negative mood shape how effectively interventions interrupt infinite scrolling on social media.

desk verdict First real field data on context-aware scrolling interventions, but the responsiveness claims are selection-conditioned and need a reanalysis before they can be taken at face value. read the letter →

arxiv 2501.11814 v2 pith:BR6ZGXLP submitted 2025-01-21 cs.HC

Luca-Maxim Meinhardt , Maryam Elhaidary , Mark Colley , Michael Rietzler , Jan Ole Rixen , Aditya Kumar Purohit , Enrico Rukzio This is my paper · ORCID
classification cs.HC
keywords infinitescrollingdigitalinterventionscontext-awarefieldstudylongitudinalreactanceresponsivenesswell-being
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 tries to show that the effectiveness of interventions against infinite scrolling on social media depends on the user's context, not just on the intervention itself. Through a 7-day field study with 72 participants who received a break prompt after 15 minutes of continuous scrolling, the authors find that being tired lowers reactance to the prompt, while being at home combined with low mood slows the user's response to it. The claim matters because current interventions are one-size-fits-all; if context systematically changes responsiveness and acceptance, interventions could be tailored to location, mood, and sleepiness to reduce regretful scrolling.

What carries the argument

The central objects are the two dependent variables: reactance (measured with the Threat subscale of the Reactance Scale for Human-Computer Interaction, five Likert items averaged per observation) and responsiveness (the elapsed time from intervention display to the user stopping infinite scrolling, log-transformed for analysis). The argument is carried by linear mixed models with a random intercept per participant fitting main and interaction effects of six contextual factors: location (at home or not), current activity, valence, sleepiness, multitasking, and social situation. The key mechanism is the interaction term, since the paper's main claim is that context factors act together rather than independently.

What would settle it

A replication that measures context at intervention time — for instance with a second prompt right at the 15-minute mark or with passive sensors — and finds that post-stop self-reports differ systematically from the state during scrolling would invalidate the causal reading of the interaction effects. So would a controlled manipulation of location and sleepiness that shows no difference in responsiveness or reactance.

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Extended reading notes

Core claim

The paper's central discovery is that intervention effectiveness during infinite scrolling is contextually modulated: users who feel sleepier experience lower psychological reactance to the break prompt, and the time it takes to stop scrolling after the prompt is shaped by interactions among several contextual factors. Specifically, low valence together with being at home slows responsiveness, while multitasking during low valence hastens it, and multitasking's speeding effect is pronounced only when away from home. These results come from a 7-day in-the-wild study with 72 participants and 927 analyzed intervention events, using linear mixed models that treat the six contextual factors both as main effects and as interactions.

Load-bearing premise

The load-bearing premise is that the context questionnaire, which participants complete only after they have stopped scrolling, accurately reports their location, mood, sleepiness, and multitasking state at the moment the intervention appeared, even though stopping may change that state.

Editorial extensions

If this is right

  • Bedtime interventions should be easier to get accepted because sleepiness lowers reactance, but a single prompt will not stop scrolling, so escalating friction is needed.
  • Users at home with low mood will tend to ignore a break prompt, so interventions in that context need stronger design friction or a different trigger.
  • Because multitasking shortens response time in low-valence and out-of-home situations, nudging users toward a secondary activity may help disengagement.
  • The presence of multiple interaction effects supports designing interventions contextually rather than treating any single factor alone.

Reading between the lines

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

  • Editorial inference: if these interaction effects are stable, intervention systems could escalate prompt strength when the user is at home and in a negative mood, using GPS and sensors as proxies for the self-reported context.
  • Editorial inference: the non-significant improvement of the responsiveness interaction model (p = .057) relative to main effects suggests the specific interaction estimates for responsiveness may not replicate; a pre-registered study powered from these coefficients would settle that.
  • Editorial inference: a direct within-subjects comparison of a fixed 15-minute prompt versus a context-adaptive prompt (e.g., shorter delay when home and low mood) would translate the correlational findings into a causal design test.
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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 / 5 minor

Summary. This paper reports a 7-day longitudinal field study with N=72 participants who installed an Android app (InfiniteScape) that detects continuous infinite scrolling in six social media apps and, after 15 minutes, displays an intervention overlay. Once a participant stops scrolling, the app administers a questionnaire about reactance and six self-reported contextual factors (current activity, social situation, at home, multitasking, valence, sleepiness), and records responsiveness as the time from intervention to stopping. Linear mixed models are used to test main and interaction effects of these contextual factors on reactance and responsiveness. The main reported findings are that sleepiness reduces reactance and that several interactions involving valence, being at home, social situation, and multitasking affect responsiveness, e.g., low valence combined with being at home slows responsiveness. The authors conclude that context-aware interventions should account for these factors.

Significance. The paper addresses a timely and practically important question: whether intervention effectiveness during infinite scrolling depends on the user's context. The study design is ambitious for the field, combining real-world tracking of a specific interaction (infinite scrolling) with event-based experience sampling over seven days, and the authors make their code and anonymized data openly available. If the central claims were supported, the findings would usefully inform the design of context-aware digital well-being interventions. However, the current analysis has two load-bearing methodological limitations—outcome-dependent selection and post-hoc self-report of context—that substantially weaken the causal interpretation of the results. The paper is transparent about these limitations in Section 5.5, but the abstract and conclusions nonetheless assert causal contextual influences that the reported models cannot identify.

major comments (4)
  1. [§4.1, §4.5.3–§4.5.5, §5.5] The responsiveness dependent variable is only observed for sessions in which the participant eventually stops scrolling and completes the questionnaire; sessions where the intervention is ignored and scrolling continues are missing. This is explicitly acknowledged in §5.5 ('we are missing data from those who continued scrolling'), but the linear mixed models in Models 3–4 condition on this selection. If context—for example, being at home with low valence—makes a user less likely to stop at all, the observed interaction effects on responsiveness in Table 1 and Figure 5 are not identified from a regression conditional on stopping. The log-transform of responsiveness does not address this issue. The manuscript needs a survival analysis treating continued scrolling as censored, or a two-part model separating the probability of stopping from the time-to-stop, to support the abstract's claim that context 'slows down responsiveness.'
  2. [§4.1, §5.5] The contextual factors (valence, sleepiness, location, multitasking, social situation, current activity) are self-reported after the participant has already stopped scrolling, not at the moment the intervention appears. The stopping event itself may change the user's affective state, sleepiness perception, or even location-related attention, so the post-stop reports may not reflect the state during the intervention. The paper acknowledges the reliance on self-report but does not discuss temporal validity of the context measures. This weakens all reported context–responsiveness associations as causal evidence; the findings should be framed as associations with post-stop context unless the questionnaire is moved to the intervention moment or validated against objective sensing.
  3. [§4.5.6, Table 1] The likelihood-ratio test comparing the interaction model (Model 4) with the main-effects model (Model 3) for responsiveness is not significant at the conventional level (χ²(60) = 78.265, p = .057), and the marginal R² of Model 4 is only 0.07. Despite this, the paper interprets five interaction coefficients from this model as robust evidence of contextual interplay. The model-comparison result and the small explained variance should temper the conclusion that context influences responsiveness; at minimum, the authors should report effect sizes and confidence intervals for the interaction terms and discuss the model-selection uncertainty explicitly.
  4. [§4.5.5, Figure 5a, Table 3] The Valence × Social Situation [Strangers] interaction is estimated from very sparse data: only 0.43% of all observations are with strangers, and the authors themselves note that no data points were obtained for low and high valence in the strangers condition. A significant interaction estimated on such sparse cells is unstable and likely to be an artifact of a few influential observations. This interaction should be either removed from the set of reported findings or clearly flagged as exploratory and unreliable.
minor comments (5)
  1. [§4.5.3] The model formula in the text uses 'Side Activity' while the table, questionnaire, and figures use 'Multitasking'; please use a single consistent term throughout.
  2. [§5.1.2] The sentence 'This observation aligns with the interaction effect between valence and multitasking (see Figure 5e)' appears to misrefer the figure; the valence × multitasking interaction is shown in Figure 5c, while Figure 5e shows the At Home × Valence interaction.
  3. [§5.1.2] The phrase 'high valance' should read 'high valence'.
  4. [§4.5.4] For the sleepiness main effect on reactance, the text reports t(917) = -3.40 and p < .001; please also report the exact p-value and a standardized effect size so readers can judge the practical importance of this effect, especially given the large sample size.
  5. [Figure 4] The x-axis of panel (b) is labeled 'log(1+coef.)' but the response variable is log-transformed; please clarify how this back-transformation relates to the model coefficients, or plot the coefficients on a common scale for easier comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central claims rest on new field data and LMM analyses, not on self-citation or definitional reductions.

full rationale

The paper's central claims—that sleepiness lowers reactance and that being at home with low valence slows responsiveness—are empirical results from a new 7-day field study (N=72, 927 data points) analyzed with linear mixed models. No equation in the paper reduces to its own input: responsiveness is objectively measured as the time from the intervention overlay to the moment the participant stops scrolling, and the contextual factors are self-reported questionnaire items that are not constructed from responsiveness. The models are fitted, not presented as predictions of held-out data, so there is no fitted-input-called-prediction pattern. The acknowledged event-based ESM limitation (§5.5: "we are missing data from those who continued scrolling and, therefore, ignored the interventions and did not answer the questionnaire") is a censoring/selection threat to the causal interpretation of the context–responsiveness interactions, but it is a statistical validity concern, not a circular derivation: the context variables are not defined in terms of the outcome, and the reported associations are not forced by construction. Self-citations to the authors' prior work, especially Rixen et al. [79], are used for factor selection, for the 15-minute intervention threshold, and for methodological precedents such as event-based ESM and the SAM valence scale. These citations are not load-bearing for the paper's novel empirical conclusion: [79] did not establish the interaction effects claimed here, and the present results depend on newly collected data. No uniqueness theorem, ansatz, or known result is imported from the authors' own prior work to foreclose alternatives. Accordingly, the derivation chain is self-contained with respect to the paper's own equations, and the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the domain assumption that self-reported context after stopping matches the context during the intervention, on the selection of participants who regret scrolling, and on the reliability of app-based scrolling detection. There are no fitted model parameters in the traditional sense; the two hand-chosen constants above (outlier threshold and intervention delay) shape the dataset. No invented entities are introduced.

free parameters (2)
  • Outlier removal z-score threshold = 3
    The authors removed data points with a z-score above 3 in responsiveness (8 points), which is a hand-chosen threshold that affects the distribution and the reported statistics (Section 4.5.1).
  • Intervention trigger duration = 15 minutes
    InfiniteScape shows the intervention only after 15 minutes of continuous scrolling, a design constant taken from prior work that defines the start of every measured responsiveness interval (Section 4.1).
assumptions (4)
  • domain assumption Post-stop self-reported context reflects context during scrolling and at intervention time.
    The questionnaire is triggered only after the user stops scrolling (Section 4.1), yet the results interpret these reports as contextual factors influencing responsiveness.
  • domain assumption Participants who self-report regret during infinite scrolling are a suitable population for evaluating interventions.
    Regret was used as an inclusion criterion based on Self-Determination Theory (Section 4.2), potentially limiting generalizability to non-regretful users.
  • standard math LMMs are robust to the remaining non-normality of the transformed dependent variables.
    The authors rely on Arnau et al. [4] to justify LMM use despite Shapiro-Wilk tests p<0.001 (Section 4.5.1).
  • domain assumption Accessibility Service window tree reliably detects infinite scrolling in all six social media apps.
    The intervention and responsiveness measurement depend on this detection logic (Section 4.1).

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Cite this review

Pith. "Pith review of Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media." pith.science (2026). https://pith.science/paper/BR6ZGXLP

@misc{pith2026250111814,
  author       = {Pith},
  title        = {Pith review of: Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BR6ZGXLP}},
  note         = {Machine review of arXiv:2501.11814}
}
read the original abstract

Infinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.

Figures

Figures reproduced from arXiv: 2501.11814 by the authors.

Figure 1
Figure 1. InfiniteScape. Including the intervention overlay [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Distribution of the investigated contextual factors during infinite scrolling interventions [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Distribution of the interventions over time of day [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Coefficients of the main effects for reactance and re￾sponsiveness. As the responsiveness was logarithmically trans￾formed, the coefficients must be considered as log(1+ coef.). Blue dots indicate a positive coefficient, orange dots negative ones. scrolling does not im…
Figure 5
Figure 5. Figure 5: Interaction effects of responsiveness with 95% CI likely accept interventions when tired. Supporting this, Chris￾tensen et al. [15] found that phone usage at bedtime is linked to poor sleep quality, which might be an issue users are aware of, thus reduc￾ing their react…
Figure 6
Figure 6. Figure 6: This plot shows the frequency of how many data [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.