Two decisions, in order: is the work web-native, and which track's reviewer pool should judge it. Both are made by the authors, both are effectively irreversible at the deadline, and the second is as consequential as the first because the venue reviews inside tracks.
Ask: if the Web's specific structure disappeared, would this contribution still make sense? Web-native work depends on at least one of: open hypertext and link structure; platform mechanics and incentives; live, adversarial, user-generated content; web-scale heterogeneity; or the socio-technical coupling of users and algorithms. A generic model that merely evaluates on a web dataset fails the test — the dataset is swappable, so an ML venue's pool serves it better.
Web-native? -> route
Contribution needs link/platform/user -> Web Conference candidate; go to
structure to exist Decision 2
Contribution is a general method; web -> NeurIPS/ICML/ICLR or KDD, cite
data is one evaluation among many web results as evidence
Contribution is about people/society, -> ICWSM or WebSci if measurement/
computation is instrumental interdisciplinarity dominates
Contribution is retrieval effectiveness -> SIGIR first; Web Conference if the
per se open-web setting changes the problem
Contribution is mining methodology with -> WSDM (methods-first) or KDD
modest web specificity (mining/deployment-first)
Contribution is a deployed web system's -> Web Conference Industry track, not
practice lessons the research tracks
The 2026 research tracks, with the question each pool is primed to ask:
| Track (2026) | The pool's primary question |
|---|---|
| Economics, online markets and human computation | Are incentives/welfare modeled, not just predicted? |
| Graph algorithms and modeling for the Web | Does the graph method respect web-graph properties? |
| Responsible Web | Are harms, fairness, or governance the contribution? |
| Search and retrieval-augmented AI | What beats strong current retrieval/RAG baselines? |
| Security and privacy | Is there a real threat model and adversary? |
| Semantics and knowledge | Does it advance KGs/ontologies/structured data on the web? |
| Social networks and social media | Is the social measurement or model construct-valid? |
| Systems and infrastructure (Web, mobile, WoT) | Does it hold under realistic load/deployment? |
| User modeling, personalization and recommendation | Is the personalization gain real and leak-free? |
| Web mining and content analysis | Is the extracted signal novel and robust at scale? |
Tie-breaks: pick the track whose evidence you actually have, not whose name flatters the abstract. A RAG-for-recommendation paper with strong offline ranking tables but no retrieval-baseline sweep survives better in "User modeling ..." than in "Search and retrieval-augmented AI." Track names are re-cut most editions — confirm the current list before deciding, and remember the cap: at most 7 submissions per author across all research tracks in 2026.
webconf-writing-style).webconf-workflow) in view.[Web-native] yes: <mechanism> / no: <reroute target>
[Track] <primary> (evidence basis); fallback <secondary>
[Lane] full / short / Web4Good / industry / workshop
[Misroute risks] <dressed-up ML / homeless interdisciplinary / track mismatch>
[Plan B] <sibling venue + what reframing it would need>