reviewpaper.ai
ModeledThe ACM Web Conference (WWW)

The WWW 2027 review form, explained

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.

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.

WWW 2027 abstract deadlineOct 18, 2026 · 23:59 AoE–d ––h ––m
Review my paper in WWW format Official source
Overall score
Overall Evaluation, -3–3
Sub-scores
None
Confidence
1–5
Form fields
8
Author checklist
5 items

How WWW reviewers read your 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.

The reviewer form, field by field

In the order reviewers see it, with every score and its official wording.

  1. 01

    Summary

    Free text

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

  2. 02

    Relevance To The Web And To The Track

    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. 03

    Resource Paper

    Single choice

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

    • no
    • yes
  4. 04

    Strengths

    List of points

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

  5. 05

    Weaknesses

    List of points

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

  6. 06

    Questions For The Authors

    List of points

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

  7. 07

    Overall Evaluation

    Score · -3–3

    Overall recommendation.

    1. 3strong accept
    2. 2accept
    3. 1weak accept
    4. 0borderline paper
    5. -1weak reject
    6. -2reject
    7. -3strong reject
  8. 08

    Reviewer's Confidence

    Score · 1–5

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

    1. 5expert
    2. 4high
    3. 3medium
    4. 2low
    5. 1none

Area chair meta-review

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

  • Accept
  • Reject

WWW submission requirements

Reviewers check these. Gaps are easy points to lose.

  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)

Policy on LLMs in reviewing

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.

Read the official policy

Author mode is for your own unpublished work. If you are an assigned reviewer, follow these rules: you are responsible for your review.

See your paper through WWW reviewers' eyes

Three simulated reviewers fill in exactly this form for your draft, then an area chair writes the meta-review. Every point links to the passage it's about. Your first three runs are free.

Review my paper — 3 free

reviewpaper.ai is not affiliated with WWW or its organizers. Forms change between cycles; always check the official source.