# WWW 2027 review form

> Simulate The Web Conference (WWW 2027) research-track reviews: Summary, relevance to the Web, Strengths, Weaknesses, Questions, the −3 to +3 overall evaluation and confidence.

- Venue: The ACM Web Conference (WWW)
- Form status: modeled, not official. The Web Conference does not publish its review form, and WWW 2027 moves from EasyChair to OpenReview. The overall-evaluation scale (−3 to 3) follows the EasyChair form seen in a public WWW 2026 review (only "2 (accept)" and "1 (weak accept)" were seen; the rest is EasyChair's default). The remaining fields are modeled on the official decision factors.
- Official source: https://www2026.thewebconf.org/calls/research-tracks.html
- Overall score: Overall Evaluation, -3–3
- Rough acceptance bar (our simulation): mean ≥ 1
- Web page: https://reviewpaper.ai/venues/the-web-conference

## How reviewers read the paper

The Web Conference decides from technical merit, originality, potential impact, quality of execution, quality of presentation, related work, reproducibility and ethics, used by ACs and SACs to make recommendations; PC chairs decide. Papers must be relevant to the Web and their track (merely using Web data is out of scope). The rebuttal is written once and is not interactive. WWW 2027 reviews on OpenReview.

- Ground every judgment in the official decision factors (merit, originality, impact, execution, presentation, related work, reproducibility, ethics).
- Check Web relevance explicitly; do not penalize a paper for the venue fit of its dataset alone.
- Professional and specific; questions should be answerable in a one-shot rebuttal.

**Where the bar sits.** WWW 2026 accepted about 676 of 3,370 research submissions (~20%). No score statistics are public; on the −3…3 scale a mean of ≥ 1 (weak accept) is a rough proxy for acceptance, 0.25–1 borderline.

## Reviewer form, field by field

### 1. Summary

Type: free text.

What the paper does and claims, in your own words.

### 2. Relevance To The Web And To The Track

Type: free text.

Is the paper's relevance to the Web and to its track stated on the first page and convincing? Papers that merely use a Web dataset or API are out of scope.

### 3. Resource Paper

Type: single choice.

Is the main contribution a resource (dataset, benchmark, tool)?

- no
- yes

### 4. Strengths

Type: list of points.

Technical merit, originality, potential impact, quality of execution and presentation, each substantiated.

### 5. Weaknesses

Type: list of points.

Specific weaknesses in technical merit, originality, execution, presentation, related work, reproducibility or ethics, most important first.

### 6. Questions For The Authors

Type: list of points.

Questions for the (non-interactive) rebuttal whose answers could change your evaluation.

### 7. Overall Evaluation

Type: score, -3–3.

Overall recommendation.

- **3**: strong accept
- **2**: accept
- **1**: weak accept
- **0**: borderline paper
- **-1**: weak reject
- **-2**: reject
- **-3**: strong reject

### 8. Reviewer's Confidence

Type: score, 1–5.

How familiar you are with the area and how carefully you checked the paper.

- **5**: expert
- **4**: high
- **3**: medium
- **2**: low
- **1**: none

## Area chair meta-review

WWW ACs and SACs recommend; PC chairs decide. The meta-review form is not public.

Recommendation options: Accept · Reject

## WWW submission requirements

1. Relevance to the Web and to the chosen track stated on the first page
2. 8 pages of main content, at most 12 pages including references and appendix
3. Double-blind; self-citations in the third person
4. Ethics/consent section with IRB details where human participants are involved
5. Code and data released after review (encouraged; optional ACM Artifacts Available badge)

## LLM policy

The WWW 2026 industry track required reviewers to commit not to rely on LLMs and forbade uploading paper content to any LLM; the research-track call states no reviewer rule, and ACM's peer-review policy forbids uploading confidential submissions to generative AI.

Official policy: https://www2026.thewebconf.org/calls/industry.html

---
Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=the-web-conference (3 free reviews). Sample report: https://reviewpaper.ai/sample.
reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. Not affiliated with any conference.