Online peer-support communities are vital for mental health, but their therapeutic benefit hinges on receiving a timely and helpful first reply. Posts that languish unanswered can exacerbate feelings of distress and abandonment. This paper develops and validates an integrated framework to predict, explain, and reduce this ``reply gap" on Reddit. First, using survival analysis on over 91,000 posts (2018–2025), we show that a deep learning model (DySurv) can accurately predict reply times (C-Index = 0.742), with a post's lexico-semantic content being a far stronger predictor than author history. Second, moving from correlation to causation, we use a causal inference framework on 48,612 posts to estimate the effect of different support types. We find that initial replies providing emotional support are most effective, increasing the odds of a positive user response by 49% (OR=1.49), an effect most pronounced for high-risk users. Third, we operationalize these insights in RiskMatch, a recommender system that routes at-risk posts to historically effective helpers. Rigorous counterfactual evaluation using inverse propensity scoring (IPS)—a method that corrects for biases in historical data—demonstrates that our system reduces the median wait time by 26 minutes for the highest-risk quintile. This work provides a validated, data-driven methodology to build more responsive and effective peer-support ecosystems, offering a concrete pathway to ensure fewer calls for help go unanswered.

When asked explicitly, a Large language model (LLM) may validate your anger-but implicitly, it may still judge that anger as inappropriate.We call this divergence the endorsementexposure gap, and it reveals that LLMs encode hidden norms about which emotions are acceptable in which contexts.To measure these norms systematically, we introduce FEELING RULES ATLAS, a benchmark of 1,320 vignettes spanning 6 institutional settings, 12 roles, 7 emotions, and 5 intensity levels.We pair the benchmark with two probes: explicit norm judgments (APPROPRIATE/INAPPROPRIATE/DEPENDS) and implicit acceptability scored by log-likelihood contrast.Across six model families, we find large cross-model variation in sanctioning thresholds and institutional "norm signatures" not reducible to overall strictness; models that appear similarly lenient explicitly can diverge sharply in implicit judgments.These results establish normative affect: contextconditioned judgments of emotional appropriateness, as a distinct alignment axis, and motivate transparent profiling of feeling rules for emotionally sensitive deployments.

In intimate messaging, how a difficult note is produced signals personal effort and authorship. We examine how AI assistance level (light tone rewrite versus heavy full draft) and a brief sender‑voiced co‑sign disclosure shape receiver attributions and outcomes in two scenarios: apology/repair and boundary requests. Study 1 (N = 152) instrumented authoring to create a curated message corpus with authoring telemetry. Study 2 (N = 704) used those messages in a randomized 2 × 2 receiver experiment (Help by Disclosure) with crossed random effects. Across scenarios, heavier drafting reliably reduced perceived ownership and authenticity; improvements in competence or clarity did not compensate. In apologies, co‑signing a tone rewrite increased authenticity and forgiveness; co‑signing a full draft slightly decreased both. In boundary requests, co‑signing was neutral to mildly negative. Stimulus‑level analyses linked concrete authoring traces to receiver judgments: more idiosyncratic “voice” cues and higher human contribution predicted higher ownership and authenticity; larger drafting distance predicted the reverse. Senders also overestimated the relational benefits of light help in boundary requests relative to receivers. We contribute: (i) scenario‑aware causal estimates of help and disclosure on ownership, authenticity, trust facets, and outcomes; (ii) an empirically grounded attributional account that clarifies why competence gains rarely offset authenticity losses; (iii) evidence of sender–receiver miscalibration under realistic disclosure mixtures; and (iv) scenario‑sensitive design guidance for voice‑preserving defaults, ownership‑restoring scaffolds, and CPM‑aligned disclosure.

Digital eco‑nudges are widely deployed, yet their long‑term efficacy, ethical acceptability, and net environmental impact remain unclear. We report two field studies targeting routine online behaviors under strict parity of message content and delivery budgets. Study 1 shows that minimal, factual tailoring improves compliance over generic prompts when opportunities are defined independently of delivery. Study 2 introduces a privacy‑preserving, on‑device contextual bandit that learns when to act and when to DoNothing, achieving higher compliance at comparable prompt intensity while maintaining autonomy. We operationalize an Ethical–Efficacy Frontier (EEF) to visualize autonomy–effectiveness trade‑offs, and compute an energy Return on Investment (ROI) that nets behavior‑driven savings against measured system overhead. Energy savings are estimated using literature‑calibrated proxies with sensitivity bands, and we discuss the energy trade‑offs of on‑device learning relative to a stylized cloud alternative. We probe short‑term persistence via withdrawal and a brief follow‑up; long‑term habit formation and rebound remain out of scope. We contribute design and reporting practices—ablation parity, opportunity denominators, EEF, and net‑impact accounting—that make digital sustainability interventions more rigorous, transparent, and respectful, advancing sustainable HCI beyond “small changes.”

AI coding assistants help, but developers still spend effort verifying model output. We isolate interface effects by holding a single LLM fixed while N = 60 participants solve three Python tasks with Inline, Chat, or Structured prompting, plus a no‑AI control. AI reduced workload by − 18.2 TLX points and time by 22% (25.0 vs. 32.1 min) and improved correctness (OR = 1.71). Within AI, Inline is fastest and lowest‑load on simple work; Chat yields higher correctness beyond a per‑observation complexity threshold (z ≈ + 0.41) without a time cost; Structured benefits novices at mid complexity. We introduce a mode‑agnostic verification‑load index (failures, time‑to‑first‑compile, churn, pauses, switches) that partially mediates rising stress/fatigue across tasks. We translate these findings into design guidance: adaptive mode orchestration, transparency on demand, and verification aware packaging, and propose reporting verification load alongside outcomes to evaluate interfaces as models evolve.

For web platforms facing regulatory scrutiny---from content moderation to ad delivery and recommendations---fairness audits routinely disagree due to metric choice, subgroup granularity, sampling variance, and dataset shift. Point estimates yield brittle pass/fail narratives that are hard to defend in governance contexts. We propose a Bayesian audit-of-audits that pools heterogeneous audits---count-based and metric-only---into interval-valued fairness claims with explicit uncertainty and policy-risk tables aligned to practitioner thresholds. The framework unifies classification and exposure metrics, enforces consistency across coarse and intersectional group definitions via soft coherence constraints, and quantifies the Value-of-Information of prospective audits. We also provide heterogeneity diagnostics and leave-one-audit-out sensitivities. Across a synthetic Audit Zoo, a content-moderation case study on CivilComments--WILDS, and an ad-delivery simulation, our meta-evaluator attains near-nominal coverage with narrower intervals and fewer decision flips than per-audit baselines, while integrating partial-information audits.

As mobile reading surges in China, platforms increasingly deploy AI-generated summaries to mitigate information overload. This study examines, among Chinese mobile readers, how summary type (AI vs. human vs. none) shapes engagement (reading time, click-through), comprehension, and trust, and when skepticism toward AI translates into opening the full article. We conducted an explanatory, mixed-methods, within-subjects experiment (N = 47) in which adults in China read six Chinese-language news articles (technology, health, lifestyle) on their own smartphones under three conditions: AI summary + “Read Full,” Human summary + “Read Full,” and No summary (full text). We logged reading time, click-through, and scroll depth; administered five-item comprehension quizzes and trust/completeness scales after each article; analyzed outcomes using repeated-measures ANOVA, linear mixed-effects models, and logistic regression; and thematically analyzed open-ended responses. Human-written summaries received higher trust and perceived completeness than AI-generated summaries, yet click-through rates were comparable (AI: 81.9% vs. Human: 72.3%). Providing the full text upfront yielded the highest comprehension; choosing to open the full article after viewing a summary largely eliminated the comprehension deficit. Topic relevance moderated the relationship between trust and click-through (lower trust increased opening only when relevance was high), and domain knowledge attenuated the comprehension penalty of summary-only reading. In on-the-go mobile contexts, an attitude-behavior gap emerges: readers who express low trust in AI summaries often still rely on them when perceived time costs of full-article reading are high. For mobile HCI and news design, clearly label AI provenance with brief coverage disclaimers, offer adaptive/layered summaries that expand based on interest signals or in high-stakes domains, and provide strategic verification prompts that nudge readers to open the full article when depth and accuracy matter.

We present Conformal@K , a model-agnostic calibration layer that provides distribution-free, finite-sample control of Top- \( K \) miss-risk in two-stage recommendation and retrieval. Given arbitrary retrieval and re-ranking scorers, Conformal@K selects monotone budgets so that, at level \(1-\alpha\) , the probability that no relevant item appears in the returned Top- \( K \) is controlled whenever a feasible parameter exists; otherwise, a transparent best-effort mode reports the residual gap with actionable diagnostics. Beyond marginal validity, we introduce overlapping-group guarantees via smoothed, self-normalized estimates, joint two-stage calibration controlling both retrieve-miss and final miss@K, and importance-weighted and windowed variants for covariate shift and temporal dependence. Empirically, on MSLR-WEB10K , Conformal@K tracks target risks across \(\alpha\) and meets global and cohort targets while preserving ranking quality. On POI recommendation ( Gowalla, Foursquare ) under an all-ranking protocol with a display cap ( \(K_{\max}=50\) ), small \(\alpha\) can be infeasible; our method still reduces global and worst-group miss-risk and improves HR@K, explicitly reporting infeasibility gaps. We compare against four fairness-of-exposure baselines, showing that Conformal@K and exposure-fair methods target complementary objectives and compose in practice. Shift-aware and streaming variants stabilize miss-risk under drift. The method drops into existing stacks with audit-friendly diagnostics.

Human–AI collaboration is increasingly integral to a variety of domains where creative ideation unfolds in iterative cycles, yet most existing studies evaluate AI-generated concepts in a single step. This paper addresses the gap by investigating “Creative Momentum Transfer”—how the timing (early vs. late) and labeling (AI-labeled vs. unlabeled) of AI prompts shape multi-round human ideation. In a between-subjects experiment (N = 247), participants proposed solutions for plastic pollution over two rounds, with AI suggestions introduced either at the outset or mid-process and labeled explicitly or not. Results reveal that early AI prompts increase overall creativity but induce stronger anchoring, whereas late AI prompts trigger a mid-round pivot that fosters more divergent thinking yet still boosts final outcomes compared to a no-AI control. Labeling amplifies both subjective and objective adoption of AI ideas, although most participants could detect AI sources even when unlabeled. Furthermore, qualitative interviews highlight nuanced perspectives on perceived ownership, authenticity, and the ways in which labeling triggers deeper scrutiny of the AI’s style. By demonstrating that baseline creativity moderates these effects more robustly than trust in AI, this study advances our theoretical understanding of multi-round human–AI synergy while offering design guidelines for next-generation creativity support systems. We discuss how user-centered design can balance rapid convergence (via early AI) with strategic pivot opportunities (via late AI) and weigh transparent labeling against ethical considerations of authorship and user autonomy.

Generative AI (GenAI) tools have rapidly transformed creative workflows by providing diverse visual outputs, yet a critical and understudied phenomenon Involves the moment-to-moment decisions that artists make to accept or dismiss machine-generated suggestions. In this mixed-methods investigation, we focus on these subtle "override" behaviors within AI-assisted illustration tasks. We recruited 57 digital artists (novice, intermediate, expert) and collected data from multiple sources: automated logs of override actions, concise in-task rationales, post-task surveys, and semi-structured interviews. Our findings indicate that stylistic discordance and technical flaws drive the highest number of rejections, though novices and experts differ markedly in both the frequency and speed of overriding. Regression analyses show that frequent dismissals strengthen perceptions of creative agency but do not consistently improve overall satisfaction; instead, the quality of final outputs emerges as the primary determinant of satisfaction. Interviews reveal how experienced artists swiftly discard suboptimal suggestions to maintain personal style, while novices oscillate between curiosity and fatigue. By mapping these decision points and their outcomes, we illuminate critical aspects of human-AI co-creation and propose design strategies—such as adaptive recommendation filtering and stage-aware interventions—to ease "override fatigue." Although this study centers on digital illustration, our approach and insights can extend to other creative or even scientific domains where iterative human-AI collaboration is essential.

There are massive classic algorithms determining whether points are inside or outside of a polygon, such as Ray Casting, Cross Product Judging and Angle-judging, whereas these algorithms require discussing various kinds of special circumstances, or the determination needs transformation into other algorithms, which are inefficient and unstable. According to the knowledge of elementary geometry and graph theory, this article suggests a new algorithm, by using the method of drawing a vertical line through the point under judgment and judging the point with the method of substitution and then marking a variable. This algorithm requires neither discussing special circumstances, nor performing division operations, therefore the efficiency and stability of it is significantly increased. Many cases have proved that the efficiency of this algorithm is about 9.5 to 9.8 times of the classical ray algorithm.