wsdm-topic-selection
GitHub用于判断项目是否适合投稿WSDM会议。通过数据相关性（Web/社交数据）和实用性原则双重测试，评估研究范围并推荐SIGIR、KDD等更合适的替代会议。
触发场景
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "wsdm-topic-selection",
"description": "Use when deciding whether a project belongs at WSDM or a neighbor - tests for the web\/social-data core and the practical-yet-principled bar, scope coverage from web search and recommendation to social networks and responsible web AI, routing against SIGIR, KDD, WWW, CIKM, RecSys, and ICWSM, and long-versus-short-track fit."
}
WSDM Topic Selection
Decide if a project is WSDM-shaped before anyone formats a page. WSDM (pronounced "wisdom") is deliberately narrow: search and data mining on the Web and the Social Web, run as a small, highly selective, traditionally single-track winter meeting jointly sponsored by four ACM SIGs (SIGIR, SIGKDD, SIGMOD, SIGWEB). The sponsorship list is the scope diagram - the venue lives at the intersection of retrieval, mining, data management, and the web itself.
The two-gate test
Gate 1 - the data gate. Is the primary object of study web or social-web data: queries and clicks, documents and links, user-item interactions, social graphs, ads, reviews, conversational sessions? A method paper whose experiments merely include a web dataset fails this gate; the web data must be what the contribution is about. Tabular-ML, vision, and generic NLP work fail here regardless of quality.
Gate 2 - the "practical yet principled" gate. The series describes its emphasis as practical yet principled approaches, and the PC enforces both adjectives:
- Practical: plausible at platform scale, aware of serving cost, evaluated on realistic interaction data.
- Principled: a nameable mechanism, bias-aware evaluation, and an explanation of why it works - not a leaderboard delta.
Projects strong on one adjective route elsewhere: principled-only theory toward theory-friendly venues, practical-only system reports toward industry tracks or applied venues.
Scope coverage check (2026 CFP areas)
The 2026 call organized scope roughly as: web search (including query analysis, evaluation, user behavior and log analysis, and explicitly "Search with Foundation Models"); web mining and content analysis (including recommender systems, crawling/indexing); social networks (link prediction, community detection, computational social science, influence, trust); fairness, accountability, and explainability for ranking, recommendation, and ads; and conversational search and assistants. If the project needs a paragraph of throat-clearing to sound like one of these, note that as a fit warning.
Routing table
| Signal in your project | Better first target | Why |
|---|---|---|
| Core IR theory, test-collection evaluation, no web-mining angle | SIGIR | Deeper IR-methods bench |
| General-purpose mining/ML method, web data incidental | KDD | Scope is data mining at large |
| Web systems, standards, platform measurement, web economics | TheWebConf (WWW) | Broader web-as-artifact scope |
| Solid applied IR/DB/mining result, breadth over selectivity | CIKM | Larger, broader program |
| Recommender-systems contribution with user-centric evaluation | RecSys | Dedicated community and review lens |
| Social-media phenomena, computational social science first | ICWSM | Social-science evaluation standards |
| Learning theory / new architecture, evaluation on static benchmarks | NeurIPS/ICML/ICLR | Method-first review culture |
| Web search/mining/rec with logs, bias-awareness, deployment realism | WSDM | This is the lane |
Tie-breakers when two venues survive: (1) whose recent proceedings contain the
papers you must cite - submit to the ongoing conversation; (2) whose review
process suits the work - WSDM's no-rebuttal, one-in-six regime punishes papers
that need explaining; (3) calendar position (wsdm-workflow maps the chain).
Selectivity realism
Anchor numbers: WSDM 2025 accepted just over 100 of more than 600 submissions (~16-17%). Single-track capacity keeps the program small by design. Honest self-assessment questions before committing the August slot:
1. Name the WSDM lineage this joins (see wsdm-related-work). [____]
2. State the behavioral fact the paper exploits or corrects. [____]
3. State the mechanism in one clause, no "framework" words. [____]
4. Which quadrant of evidence is weakest (wsdm-experiments)? [____]
5. Would the industry half of the PC call the setting realistic? [____]
6. Is there a reason to *choose* this paper, not just no flaw? [____]
Blank boxes at question 1-3 usually mean the project is a neighbor-venue paper wearing WSDM formatting. A weak answer at 6 with strong answers elsewhere suggests the short-paper track (since 2026) over the long track.
Routing vignettes (fictional)
- A contrastive-learning objective evaluated on MovieLens and two vision benchmarks. Fails Gate 1 - web data is a test case, not the object. Route to the ML flagships; return to WSDM only if a version emerges whose claims are about interaction data specifically.
- A measurement study of coordinated inauthentic amplification on a microblog platform, with a detection heuristic. Passes Gate 1; Gate 2 depends on the mechanism. If detection is principled and evaluated for ranking/mining impact, WSDM fits; if the contribution is the social phenomenon itself, ICWSM's review culture will value it higher.
- An LLM-based relevance judge replacing crowd labels, validated against human judgments on public query sets. Passes both gates via the evaluation lineage; equally at home at SIGIR - apply tie-breaker (1): wherever the papers it must cite appeared last two years.
- A vector-database sharding scheme with a web-corpus benchmark. The contribution is data management; the web corpus is incidental. VLDB-family first, WSDM only with a retrieval-behavior angle.
Foundation-model-era fit
LLM work is in scope only through the web lens - the 2026 CFP names "Search with Foundation Models" and the 2026 Industry Day theme was LLMs and agentic AI in industrial settings. The test: does the contribution concern how foundation models interact with web-scale search, recommendation, or user behavior (retrieval-augmentation for search, LLM-based ranking or judgment, agentic browsing, synthetic-content effects on ranking ecosystems)? A prompt technique evaluated on static QA benchmarks fails Gate 1 regardless of the word "search" in its title.
Output format
[Gate 1] web/social data is the object of study: pass / fail (reason)
[Gate 2] practical + principled both present: pass / weak adjective named
[Scope area] 2026-CFP area matched: <area or none>
[Routing] WSDM / <neighbor> with tie-breaker rationale
[Track] long / short / demo / cup / defer a year
[Confidence] choose-this-paper reason in one sentence
版本历史
- 9f86f09 当前 2026-07-19 17:55
