1 Introduction↩︎

Social media platforms operate under competing and often contradictory logics: the pursuit of user engagement and the need to maintain a healthy online environment [1]. Content moderation serves as a central mechanism through which platforms attempt to reconcile these tensions by regulating the visibility and circulation of user-generated content. Moderation practices take multiple forms, including shadowbanning, deplatforming, and content removal [2]–[4]. Among these, content removal has received substantial scholarly attention, particularly regarding its effectiveness in promoting user compliance and sustaining engagement [5], [6].

However, existing research has largely treated content removal as a uniform intervention, overlooking the diversity of moderation practices that have emerged in contemporary platforms. In practice, moderation is carried out by a range of actors, including automated systems, individual human moderators, and collective moderator accounts, each operating under different incentives and constraints [7]. Moreover, the objectives of moderation vary widely, from preventing spam and enforcing formatting rules to addressing misinformation and harmful content [8]. As a result, the effectiveness of who moderates and for what purpose remains underexplored. A more nuanced understanding of these distinctions is critical for identifying how platforms can better balance their competing logics of engagement and governance.

Content moderation is increasingly a collective effort between humans and automated systems, and thus should be understood as a form of human–computer interaction (HCI). Prior research suggests that transparency in moderation, such as providing explanations for content removal, can improve user engagement and perceptions of fairness [9]. At the same time, the growing prevalence of social bots and AI systems has made automated moderation ubiquitous [10]. Consequently, users now receive moderation messages from both human and non-human agents, challenging existing theoretical frameworks in HCI.

Traditional perspectives offer only partial explanations for this dynamic. The Computers as Social Actors (CASA) framework conceptualizes computers as entities that elicit social responses from users [11], whereas Computer-Mediated Communication (CMC) frameworks position computers as channels facilitating human–human interaction [12]. With the rise of AI and increasingly agentic systems, these distinctions are becoming blurred. Contemporary computational agents do not merely trigger social responses or mediate communication; they actively participate in communicative processes and shape interaction outcomes [13]. In the context of content moderation, the coexistence of human and automated moderators reflects this shift toward hybrid communicative agency.

This study addresses two key gaps in the literature. First, prior research has largely overlooked the heterogeneity of moderation practices, treating content removal as a monolithic intervention despite variation in moderator identity, intent, and communication style. Second, there is limited theoretical understanding of how users respond to moderation in hybrid human–AI interaction contexts, where communicative agency is distributed across human and automated actors. By examining how different moderator roles, moderation purposes, and linguistic features jointly shape user behavior, this study provides a more nuanced account of moderation effectiveness.

Ultimately, this work aims to advance both theory and practice by identifying the conditions under which content moderation can effectively balance platform governance with user engagement.

2 Literature Review↩︎

2.1 Content Moderation↩︎

Social media platforms face a fundamental tension between enabling open participation and maintaining healthy discourse, making content moderation a necessary but consequential mechanism of platform governance [14]–[16]. Research consistently shows that moderation is not simply corrective: community-level interventions such as bans and quarantines often displace rather than eliminate harmful behavior [17]–[20], while at the individual level, moderation can generate chilling effects, reduce participation, encourage coded language, and in some cases intensify engagement with extreme content [21]–[23]. These outcomes suggest that how moderation is implemented matters as much as whether it is applied.

A key mechanism underlying these effects is users’ perception of fairness, transparency, and control. Content removal raises longstanding concerns about platform governance legitimacy [16], [24], [25], and users who feel their expression is constrained often experience reduced participation and diminished agency [3], [26]. How moderation is communicated plays a critical role here: directing users to generic community guidelines reduces perceived fairness, whereas transparent explanations improve trust [27]. [28] further demonstrate that trust in moderation depends on heuristic cues triggered by the perceived source, and that enabling user feedback enhances perceived agency. Although providing removal explanations can encourage continued engagement [9], it remains unclear whether this reflects genuine norm compliance or strategic circumvention. Together, these findings point to a critical gap: existing work remains focused on perceptual outcomes rather than on the direction and quality of post-moderation behavior.

2.2 Human AI Interaction and Content Moderation↩︎

The TIME framework conceptualizes two routes through which users interact with media: a cue route, whereby interface cues activate heuristic processing that shapes perceptions and judgments, and an action route, whereby feature use shapes users’ sense of agency and behavioral engagement [29]. HAII-TIME extends this to human and AI interaction contexts by emphasizing how perceived agency, whether attributed to human, artificial, or hybrid actors, shapes interpretation and evaluation of system outputs [13]. Content moderation is a particularly apt context for this framework: moderation decisions attributed to human moderators, automated systems, or ambiguous collective accounts may lead users to form different interpretations of the legitimacy, flexibility, and negotiability of the intervention [4], [16]. While prior HAII-TIME applications have focused on perceptual outcomes such as trust [28], less is known about how these cue-based processes translate into behavioral responses, or how the communicative features of moderation messages shape behavioral adaptation over time [6].

To address this gap, we conceptualize post-moderation behavior through three theoretically grounded trajectories. The first, self-censorship, refers to a sustained reduction in posting activity following moderation, reflecting diminished user agency consistent with self-determination theory’s prediction that reduced autonomy produces disengagement [30], and with chilling effects research showing that perceived enforcement leads individuals to suppress expression even without direct sanction [31], [32]. The second, compliance, refers to continued participation with behavioral adjustment, reflecting agency negotiation consistent with procedural justice theory, which holds that individuals comply more readily with rules they perceive as legitimate and fairly enforced [33]. The third, resistance, refers to continued rule violation following moderation, reflecting reactance against externally imposed constraints, consistent with psychological reactance theory’s prediction that perceived threats to freedom motivate oppositional behavior [34], [35]. Linguistic features of moderation messages provide a further layer of influence, shaping how interventions are processed through heuristic cues or deeper elaboration [13], [36], [37].

This framework motivates three research questions and one hypothesis. We predict that behavioral trajectories differ systematically across moderation sources:

H1: Users’ post-moderation behavioral trajectories differ across sources of agency (e.g., bot, modteam, and personal account moderators).

Beyond source, violation type may signal varying levels of severity and interpretability that interact with source effects to shape behavior:

RQ1: How does the source of moderation interact with moderation context to shape users’ post-moderation behavioral trajectories?

The linguistic style of removal explanations may further shape responses through heuristic or elaborative processing:

RQ2: How are linguistic features of moderation explanations associated with users’ post-moderation behavioral trajectories?

RQ3: How do linguistic features moderate the relationship between moderation context, source of agency, and users’ post-moderation behavioral trajectories?

3 Methods↩︎

3.1 Data↩︎

We use Reddit data spanning January 2021 through December 2025. Reddit is particularly well-suited for studying online moderation because its structure makes moderation actions and moderator identities directly observable, allowing us to link moderation events to subsequent user behavior at scale. Our data is obtained through academic torrent by [38]. Our platform-wide sample includes 11,795,036 moderation events across 9,285,410 users and 61,261 subreddits. Because Reddit moderators frequently leave removal explanation comments addressed to specific users, we are able to reconstruct individual-level behavioral trajectories before and after moderation, which is rare in observational platform data.

3.2 Labeling↩︎

Moderator Classification. We extract all comments tagged as moderator actions and classify them into three categories: (1) bot accounts, identified by usernames containing “bot” or “auto”; (2) modteam accounts, identified by usernames containing “modteam”; and (3) personal accounts, which show no significant automated indicators and are presumed to be human-operated. This distinction matters because automated and team-based moderation may carry different social signals than moderation from a visible personal account, potentially producing different behavioral responses. We acknowledge that username-based classification is a heuristic proxy and may introduce some misclassification, particularly for bots using atypical naming conventions; however, this approach is consistent with prior computational work on moderator identity on Reddit [9], [23].

Removal Explanations. We extract all posts containing keywords indicative of content removal, excluding posts that resemble generic welcome messages for new members, as those do not constitute moderation actions directed at a specific user’s behavior. All extracted posts were manually verified by a human annotator to confirm labeling accuracy. See Appendix for detailed explanation categorization procedure.

Identifying Moderated Users. We apply regular expression (regex) matching to identify instances where a removal explanation message references a username, specifically, patterns beginning with /u or /u/ that are not embedded within a hyperlink. This allows us to directly link a moderation event to the specific user whose content was removed, which is necessary for tracking subsequent behavioral trajectories.

Classifying Moderation Reasons. We classify moderation reasons using keyword snowballing, iteratively expanding a seed set of keywords to capture the range of rule violations in the data. Keywords were then manually grouped into thematic categories by the research team. Capturing why a user was moderated is essential for isolating whether different violation types produce systematically different behavioral responses, as theorized in RQ1.

3.3 Measures↩︎

Posting Frequency Change↩︎

We define two complementary metrics to capture changes in user posting behavior before and after moderation. Using multiple metrics is intentional: each captures a different dimension of behavioral response, and relying on a single measure risks either privileging highly active users or obscuring meaningful variation among less active ones. Throughout, \(N_{\text{before}}\) and \(N_{\text{after}}\) denote the number of posts in a fixed window before and after moderation, respectively. We add 1 to \(N_{\text{before}}\) in all measures to account for the removed post itself as part of pre-moderation activity and to avoid division by zero for users with no prior posts in the window.

Direct Difference measures the absolute change in posting volume: \[\Delta_{\text{post}} = N_{\text{after}} - (N_{\text{before}} + 1)\] This captures the raw magnitude of behavioral response, but because it is not normalized, highly active users can dominate group-level estimates.

Log Ratio measures proportional change in posting relative to baseline: \[\text{Log Ratio} = \log\!\left(\frac{N_{\text{after}}}{N_{\text{before}} + 1}\right)\] The log transformation symmetrizes the metric around zero and reduces the leverage of users with very high baseline activity, making it our primary metric for operationalizing self-censorship (see below).

Moderation Rate↩︎

We measure the rate of subsequent moderation actions a user receives after their first removal: \[r = \frac{N_{\text{additional moderations}}}{N_{\text{posts after first moderation}} + N_{\text{additional moderations}}}\] Rather than using raw counts, we normalize by total post volume to ensure comparability across users who post at different frequencies. A user moderated 3 times out of 6 posts reflects a qualitatively different behavioral pattern than one moderated 3 times out of 300.

See appendix for all detailed distribution and threshold decision making.

Probabilistic Behavior Classification via Sigmoid Mapping↩︎

Rather than applying hard thresholds to classify behavioral outcomes, we use a sigmoid function to map behavioral metrics into continuous probability scores. This approach acknowledges that behavioral categories such as self-censorship and resistance exist on a continuum rather than as discrete states, and avoids the sensitivity to threshold placement that binary classification entails. The sigmoid function is defined as: \[\text{sigmoid}(x) = \frac{1}{1 + e^{k(x - \text{threshold})}}\] where \(k\) controls the steepness of the transition and \(\text{threshold}\) defines the decision boundary. We parameterize the slope \(k\) using the interquartile range (IQR) of the observed distribution: \[k = \frac{2}{\text{IQR}}\] Anchoring \(k\) to the IQR ensures the sigmoid’s transition region spans the central mass of the data, where behavioral differences are most ambiguous and gradual probability assignment is most appropriate. This also makes the classifier adaptive to each metric’s empirical distribution rather than imposing an arbitrary fixed slope.

Measuring Self-Censorship↩︎

We operationalize self-censorship as a sustained reduction in posting activity following moderation. We use log ratio as the input metric because its distributional properties, such as symmetry around zero and reduced skew, make population-level thresholds more stable and interpretable. The decision boundary is set at the empirical 5th percentile of the population’s monthly log-ratio distribution, capturing users whose posting decline is more extreme than 95% of the general user population. This conservative threshold reflects our theoretical commitment to distinguishing genuine behavioral withdrawal from ordinary fluctuations in posting frequency. The resulting classifier is: \[P(Y = \text{self-censor} \mid \text{log ratio}) = \frac{1}{1 + e^{1.443(x + 1.846)}}\]

Measuring Resistance↩︎

We operationalize resistance as a persistently elevated moderation rate following an initial moderation event. The decision boundary is set at the 95th percentile of the population’s moderation rate distribution, so that only users whose re-moderation rate is unusually high relative to the broader user base are classified as likely resistant: \[P(Y = \text{resistant} \mid r) = \frac{1}{1 + e^{-0.2(r - 0.5)}}\] Because the empirical moderation rate distribution is heavily zero-inflated, with both the 25th and 75th percentiles equal to zero, reflecting that most users are not re-moderated. Thus, the standard IQR-based slope is undefined. We therefore replace the degenerate IQR with a small upper bound of \([0, 0.1]\) and set \(k = -20\), producing a steep but numerically stable sigmoid that closely approximates the intended hard threshold.

Measuring Compliance↩︎

We define compliance as the joint absence of self-censorship and resistance, reflecting users who neither disengage from the community nor persist in rule-violating behavior. Rather than treating compliance as an independent third category, we derive it from the existing classifiers to ensure logical consistency across all three behavioral outcomes: \[P(Y = \text{compliance}) = (1 - P(\text{self-censor})) \times (1 - P(\text{resistant}))\]

3.4 Analysis↩︎

Our analytic strategy maps directly onto the study’s hypotheses and research questions. To test H1, which predicts differences in post-moderation behavioral trajectories across moderator types, we use one-way ANOVA to compare behavioral outcomes across bot, modteam, and personal account conditions. To address RQ1, which examines the interaction between moderation source and moderation context, we extend the ANOVA framework by incorporating moderation reason as a grouping factor, allowing us to assess whether source effects are consistent across violation types or contingent on context. For RQ2, which examines the relationship between linguistic features of moderation explanations and behavioral trajectories, we use linear regression with LIWC-derived features as predictors. Because LIWC produces a high-dimensional feature space, we first filter out features with a median of zero to remove variables with insufficient variation for meaningful analysis. We then apply Principal Component Analysis (PCA) to reduce dimensionality and surface latent linguistic dimensions that capture the most variance in the data. The resulting principal components serve as predictors in linear regression models, allowing us to interpret the relationship between linguistic style and post-moderation behavior in a lower-dimensional, less collinear space. Finally, to address RQ3, which examines how linguistic features moderate the relationship between source, context, and behavioral trajectories, we introduce interaction terms between the PCA-derived linguistic components and the moderation source and context variables in the regression models.

3.5 Results↩︎

Table 1 presents pairwise differences in behavioral outcomes across moderator types, offering support for H1. Bot moderation produces the highest compliance and the lowest self-censorship relative to both human moderator types, suggesting that users interpret automated enforcement as impersonal and rule-bound rather than as a social judgment. The strongest self-censorship effect emerges among modteam-moderated users, who show substantially higher withdrawal rates than both bot-moderated users (mean difference \(= 0.085\), \(p < .0001\)) and personal account-moderated users (mean difference \(= 0.013\), \(p < .0001\)). Personal account moderation occupies an intermediate position (mean difference from bots \(= 0.072\), \(p < .0001\)). For compliance, bots outperform modteam by \(0.078\) and personal accounts by \(0.045\) (both \(p < .0001\)), while modteam and personal accounts converge at similar levels, suggesting that the human-versus-automated distinction drives compliance differences more than variation within human moderator types. Resistance differences reach statistical significance across all comparisons but should be interpreted with caution, as all groups share a median resistance value of zero, and differences most plausibly reflect large-sample sensitivity to distributional shape.

To address RQ1, Figure 2 presents mean behavioral probabilities across violation categories and moderator types. Kruskal-Wallis tests confirmed significant differences across moderation reasons for all three outcomes (self-censorship: \(H = 217{,}212.25\), \(p < .0001\); resistance: \(H = 29{,}907.54\), \(p < .0001\); compliance: \(H = 250{,}207.77\), \(p < .0001\)). A clear severity gradient emerges: harmful or illegal content produces the highest self-censorship (\(M = 0.145\)), the highest resistance (\(M = 0.097\)), and the lowest compliance (\(M = 0.765\)), while formatting or structure violations produce the lowest self-censorship (\(M = 0.081\)) and highest compliance (\(M = 0.878\)). The moderator source effect persists across all violation categories but widens with violation severity: the gap between bot and human moderator types is modest for formatting violations but substantially larger for harmful or illegal content. Resistance findings again warrant caution given near-zero median values (\(\tilde{x} = 0.000045\)) across all categories.

To address RQ2, Figures ¿fig:fig:main95HL? and ¿fig:fig:main95CH? display the main effects of high-level (HL) and child-level (CH) principal components on behavioral outcomes, with filled bars indicating effects surviving FDR correction (\(q < .05\)). A consistent pattern emerges: formal, authoritative, or elaborated communication tends to increase resistance, while warm, prosocial, or positively framed communication tends to support compliance and reduce resistance. Among the HL PCs, Community-Scale Numerical Reference (HL-PC9; \(\hat{\beta} = 0.443\), \(q < .05\)), Conversational vs.Analytical Register (HL-PC1; \(\hat{\beta} = 0.313\), \(q < .05\)), and Message Length and Elaboration (HL-PC8; \(\hat{\beta} = 0.271\), \(q < .05\)) show the strongest resistance-increasing effects, while Descriptive Positive Framing (HL-PC12; \(\hat{\beta} = -0.443\), \(q < .05\)) and Warm Social Tone (HL-PC3; \(\hat{\beta} = -0.190\), \(q < .05\)) are associated with lower resistance. Among the CH PCs, Explanatory Causal Reasoning (CH-PC5; \(\hat{\beta} = -0.226\), \(q < .05\)) and Achievement Positive Framing (CH-PC7; \(\hat{\beta} = -0.295\), \(q < .05\)) are the strongest resistance-reducing dimensions, while Direct Positive Address (CH-PC2; \(\hat{\beta} = 0.249\), \(q < .05\) for resistance; \(\hat{\beta} = -0.051\), \(q < .05\) for self-censorship) presents a nuanced pattern: warm direct engagement simultaneously reduces withdrawal while provoking resistance.

To address RQ3, Figures ¿fig:fig:interaction95HL? and ¿fig:fig:interaction95CH? display effective slopes across violation categories. Of 480 interactions tested, only 33 survive FDR correction, a sparsity that is itself informative: linguistic effects are largely consistent across moderation contexts, with meaningful exceptions concentrated around harmful or illegal content and formatting violations. Notably, several features that show significant main effects do not produce significant interactions with context. Warm Social Tone, for instance, reduces resistance as a main effect but does not emerge as a significant moderator within any specific violation category, suggesting its benefit is diffuse rather than context-specific and may be diluted when violation severity introduces competing processing demands.

The distribution of significant interactions also reveals important asymmetry across violation types. Account issues attract the largest number of significant linguistic features that can be leveraged to improve compliance through removal explanation messages, offering moderators a relatively rich toolkit of communicative strategies. In contrast, harmful or illegal content has very few linguistic features that reliably improve outcomes. For this most severe violation category, the clearest interaction effects involve features associated with negative tone and past-focused reflection: Emphatic Negative Past-Focused Reflection (CH-PC14) amplifies self-censorship for harmful content (\(\Delta = +0.10\), \(q < .05\)) but shows little effect on compliance and no significant effect on resistance. This asymmetry suggests that for the most serious violations, moderation messages can stop users from continuing to participate, but cannot readily redirect them toward compliant re-engagement. Harmful content violations, in other words, appear stoppable but not easily changeable through linguistic intervention alone.

Among the high-level PCs, Message Length and Elaboration (HL-PC8) dramatically amplifies resistance for content quality violations (effective slope \(= 0.80\), \(\Delta = 0.62\), \(q < .01\)) and harmful or illegal content (effective slope \(= 0.90\), \(\Delta = 0.71\), \(q < .05\)) compared to \(0.19\) for account issues, while Community-Scale Numerical Reference (HL-PC9) produces the largest resistance effect in the model for harmful content (effective slope \(= 1.14\), \(\Delta = 0.80\), \(q < .05\)). Logical Connective Elaboration (HL-PC11) reveals a sign reversal, increasing resistance for account issues but reducing it for formatting violations (\(\Delta = -0.55\), \(q < .05\)), consistent with the interpretation that reasoned explanations are only effective when violations have clear, correctable rules. At the child level, Emphatic Prosocial Exclamation (CH-PC9) nearly triples its resistance-reduction effect for harmful content (effective slope \(= -1.05\) vs. \(-0.32\) for account issues, \(\Delta = -0.73\), \(q < .05\)), while Direct Positive Address (CH-PC2) consistently amplifies resistance across serious violation categories, rising from \(0.31\) for account issues to \(0.71\) for both rule or scope violations and harmful content. Together, these findings confirm that the effectiveness of linguistic style is highly contingent on violation context, and that strategies effective in routine moderation can backfire for serious violations.

4 Discussion↩︎

This study examined how moderation source, violation context, and linguistic features jointly shape post-moderation behavioral trajectories on Reddit. Drawing on HAII-TIME [13], we theorized that moderation conveys agency cues producing divergent behavioral responses through psychological reactance [34], [35], procedural justice [33], and self-determination theory [30].

Bot moderation consistently produces higher compliance and lower self-censorship than both personal account and modteam moderation, challenging the assumption that human agency cues are inherently advantageous [11], [13], [28]. We interpret this as an impersonality advantage: automated attribution signals rule-bound, non-judgmental enforcement, reducing the personal threat that drives reactance and autonomy loss [30], [34], [39]. Modteam accounts produce the strongest self-censorship through what we term institutional depersonalization: collective accounts are neither clearly human nor clearly automated, making appeal feel impossible and producing withdrawal from perceived absence of agency [4], [16], [30].

Linguistic cues do not operate uniformly across violation contexts but are moderated by violation severity, extending HAII-TIME [13] with a violation salience moderator. We observe an irony of effort: elaborate messages amplify resistance for serious violations, suggesting that extensive justification inadvertently signals intervention gravity and triggers reactance [34], [35], whereas reasoned explanations improve compliance only when violations have clear, correctable rules [9], [33]. Several features with significant main effects, including Warm Social Tone, lose significance when violation context is accounted for, suggesting their benefits are diffuse rather than context-specific. More broadly, account issues offer moderators a rich linguistic toolkit for shaping behavior, while harmful or illegal content offers very few effective levers. For the most serious violations, moderation can suppress participation but cannot redirect users toward compliance: such violations are stoppable but not easily changeable through linguistic intervention alone. Platforms should therefore not expect message design to rehabilitate users who post harmful content, and should instead invest in account-level interventions and graduated enforcement pathways [16], [40]. For lower-severity violations, context-adaptive messaging, short prosocial framing for serious cases and logically elaborated guidance for procedural ones, represents a tractable intervention [9], [41].

4.1 Limitations and Future Directions↩︎

Several limitations warrant acknowledgment. First, our outcomes are behavioral rather than perceptual: we observe downstream traces of reactance and legitimacy judgments without measuring underlying psychological mechanisms. Future work combining observational data with experimental methods could address this. Second, the observational design precludes causal inference; we treat this as a quasi-natural experiment, and future experimental work would strengthen causal claims. Third, our moderator classification relies on username heuristics that may introduce measurement error, and Reddit’s unusually transparent moderation structure may limit generalizability to platforms where moderation is less visible.

4.2 Ethical Considerations↩︎

This study carries potential risks that warrant acknowledgment. The findings could be misused by platform operators to engineer moderation messages that prioritize retention over genuine norm compliance or user wellbeing. We emphasize that our findings are intended to inform transparent, fairness-oriented moderation design rather than optimize behavioral outcomes as ends in themselves. The asymmetry we document between violation types could also be interpreted as justification for harsher enforcement of serious violations; we caution against this reading, as our findings speak to the limits of linguistic intervention rather than the appropriate scope of punitive action [16], [25]. Finally, findings may not generalize to platforms serving more vulnerable populations, where the consequences of moderation are likely more severe and where design implications should be applied with particular care.

5 Appendix↩︎

6 Data Overview↩︎

7 Method↩︎

7.1 Distribution of Baseline user activity↩︎

7.2 Principle Component Selection↩︎

8 Result↩︎

8.1 ANOVA and Dunn post-hoc analysis on different behavioral trjactories when moderated by different source↩︎

Appendix: Statistical Tests by Metric↩︎

Metric: Self-Censor↩︎

The Kruskal-Wallis test revealed a significant difference across moderator groups (\(H = 663{,}237.06\), \(p < .0001\)). Post-hoc Dunn tests with Bonferroni correction confirmed that all pairwise comparisons were significant (\(p < .0001\)).

Metric: Resistance↩︎

The Kruskal-Wallis test revealed a significant difference across moderator groups (\(H = 146{,}970.87\), \(p < .0001\)). Post-hoc Dunn tests with Bonferroni correction confirmed that all pairwise comparisons were significant (\(p < .0001\)).

Note: All groups share a median of 0; significant differences detected by the Kruskal-Wallis and Dunn tests reflect differences in distributional shape and higher moments rather than central tendency.

Metric: Compliance↩︎

The Kruskal-Wallis test revealed a significant difference across moderator groups (\(H = 330{,}201.54\), \(p < .0001\)). Post-hoc Dunn tests with Bonferroni correction confirmed that all pairwise comparisons were significant (\(p < .0001\)).

9 Linguistic regression results↩︎

AMain Effects Results↩︎

The following tables report logistic regression main effects for each outcome (Self-Censor, Resistance, Compliance) and each PCA set (HL: High-Level, CH: Children). Columns show the coefficient (\(\hat{\beta}\)), standard error, \(z\)-statistic, \(p\)-value, 95% confidence interval, and FDR-adjusted significance. \(✔\) denotes FDR \(q<.05\) for linguistic PCs (reported where applicable).

BReference Category Effects (Account Issues)↩︎

The following tables report the effects of each linguistic PC on the three outcomes when moderation reason is the reference category (account issues). All models use Bonferroni-corrected FDR at \(q<.05\).

CEffective Slopes by Moderation Reason↩︎

Effective slopes represent the total effect of each linguistic PC on each outcome within each moderation reason category (main effect \(+\) interaction). The reference category is account issues. Interaction \(p\)-values test whether the slope differs significantly from the reference slope.

DFDR-Corrected Interaction Terms↩︎

The following tables list all PC \(\times\) moderation-reason interaction terms from the logistic models, with Benjamini–Hochberg FDR correction applied within each outcome \(\times\) PCA-set block. \(✔\) denotes \(q_\mathrm{FDR}<.05\).

References↩︎