Web Conference Experiments

The empirical bar at this venue is not "more datasets" — it is evidence whose scale, freshness, and realism match the claim. A method claiming web-scale efficiency must show the scaling curve; a measurement claiming platform-general behavior must show more than one platform; a system claiming deployability must show cost under realistic load. Audit the claim-evidence match before adding anything.

Claim-to-evidence contract

Dataset selection with provenance

• Prefer datasets with documented collection: source, crawl window, sampling rule, known biases. An undocumented Kaggle mirror of a platform dump is a provenance liability reviewers now flag.
• Freshness is evidential here: user behavior and platform mechanics drift, so a 2015 interaction dataset supports historical claims, not claims about current platforms. Either add a recent corpus or scope the claim in time.
• Report per-dataset statistics (nodes/edges/users/items/time span) in a table, and state why this set of datasets spans the claim — heterogeneity in domain, size, and density is the point, not the count.
• For data you collected: consent/ToS posture and aggregation level belong beside the dataset description (body text, per webconf-writing-style).

Leakage: the venue's most-caught methodological bug

Web data is temporal, popularity-skewed, and duplicated across the crawl. Three specific leaks to audit:

1. Temporal: random splits on interactions or evolving graphs train on the future. Use a time-based split rule and state it ("train < T ≤ test").
2. Popularity: negative sampling that mirrors test popularity inflates ranking metrics; report at least one popularity-debiased or full-ranking metric.
3. Duplication/contamination: near-duplicate pages across splits, and — for LLM-era pipelines — benchmark text present in a foundation model's pretraining. State the dedup rule; where an external LLM is a component, discuss contamination explicitly rather than hoping no one asks.

Baselines and ablations

Baselines come from the sibling circuit's current cycle (WWW, WSDM, SIGIR, KDD, CIKM within ~2 years), tuned with the same budget as your method — state the search space for both, since asymmetric tuning is the most common quiet unfairness. Ablate the claimed mechanism, not everything: if the paper's story is "the crawl-time signal is what helps," the ablation table must contain exactly the row that removes it.

Experiment matrix skeleton (fill per claim, not per dataset):
  claim C1 "quality"  : datasets D1-D3 x {ours, B1..B4} x 5 seeds -> mean±sd, test
  claim C2 "scales"   : D_synthetic sized 10^6..10^9 edges x {ours, B1} -> cost curve
  claim C3 "mechanism": ours minus {signal S, module M} on D1-D3    -> delta table
  claim C4 "external" : platform P2 replication of headline result  -> one table
Every table row answers a named claim; rows answering nothing get cut.

Statistics that survive review

• Repeated runs with different seeds wherever training is stochastic; report mean ± sd and the number of runs.
• Significance tests matched to the design (paired across datasets/queries), with effect sizes — at web scale everything is "significant," so the delta's magnitude and cost carry the argument.
• Percentiles, not means, for latency and exposure-type quantities; web distributions are heavy-tailed and means mislead.

When offline evidence is not enough

Claims about user response (satisfaction, engagement shifts, behavior change) need a user study or an online experiment, with ethics posture stated; claims about production viability need load realism. If neither is obtainable, weaken the claim to what offline evidence supports — "improves offline ranking quality" is publishable; "improves user satisfaction" without users is a rebuttal-week wound that cannot be healed in seven days.

Predictable objections and their cheap preemptions

Four objections recur across this venue's tracks; each has a preemption that costs a sentence or a table row, not a new experiment campaign:

• "Gains may come from capacity, not the proposed component" — include one parameter-matched ablation row and state parameter counts in the caption.
• "Datasets are all from one platform family" — either add the second-family corpus or write the scope sentence in the conclusion; silence converts a limitation into a discovered flaw.
• "The baseline numbers differ from the original paper" — declare the re-implementation and the protocol difference (split, metric variant, preprocessing) in a footnote at first mention; unexplained deltas read as either sloppiness or gaming.
• "Efficiency claims lack a cost axis" — every quality table involving a method whose selling point includes scale gets a companion column (time, memory, or queries), because at web scale a 0.5-point win at 10x cost is a negative result.

The meta-rule: reviewers here are drawn from a circuit that reviews the same methods at WSDM, SIGIR, and KDD in the same year; they have seen this quarter's common failure modes several times already. The paper that names its own weaknesses first is the one whose rebuttal week is quiet.

Audit checklist

• Every headline claim mapped to a table/figure that can carry it.
• Split rules stated; temporal/popularity/duplication leaks addressed.
• Baselines recent, tuned symmetrically, search spaces disclosed.
• Variance, tests, and effect sizes present; percentiles for tails.
• Scope sentences where evidence is single-platform or offline-only.

Output format

[Claim-evidence map] C1..Cn -> table/figure or GAP
[Leakage audit] temporal / popularity / duplication: clean or findings
[Baseline fairness] recency, tuning symmetry: pass/fail
[Scale realism] does evidence scale match claim scale? <notes>
[Required additions] <ranked by review risk, with cost estimate>