Conference Information

WSDM 2026: International Conference on Web Search and Data Mining

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Submission Date:
2025-08-07
Notification Date:
2025-10-23
Conference Date:
2026-02-22
Location:
Boise, Idaho, USA
Years:
19
CCF: B   ICORE: A   QUALIS: B1   Viewed: 316921   Tracked: 317   Attend: 46

Call For Papers

WSDM 2026 (International Conference on Web Search and Data Mining) is a CCF B / ICORE A / QUALIS B1 conference held in Boise, Idaho, USA on 2026-02-22. The paper submission deadline is 2025-08-07. Acceptance notifications are sent on 2025-10-23.

List of Topics Papers accepted to WSDM will be published in the ACM Digital Library. We invite submissions on a broad range of topics related to search and data mining on the Web and Social Web. Areas of interest include, but are not limited to, the following topics: Web Search Algorithms for web-scale search; Adversarial search; Search user interfaces and interaction; Distributed search, metasearch, peer-to-peer search; Local and mobile search; Multimedia web search, cross-lingual search; Query analysis and query processing; Search benchmarking and evaluation; Search user behavior and log analysis; Search with Foundation Models Web Mining and Content Analysis Crawling and indexing web content; Web recommender systems and algorithms; Clustering, classification, and summarization of web data; Data, entity, event, and relationship extraction; Knowledge acquisition and automatic construction of knowledge bases; Large-scale graph analysis; Semantic search, faceted search, and knowledge graphs; Multimodal data mining; Scalable algorithms for mining web data; Opinion mining and sentiment analysis; Web traffic and log analysis; Web measurements, web evolution, and web models. Web of Things, Ubiquitous and Mobile Computing Algorithms for web-scale search; Adversarial search; Search user interfaces and interaction; Distributed search, metasearch, peer-to-peer search; Local and mobile search; Multimedia web search, cross-lingual search; Query analysis and query processing; Search benchmarking and evaluation; Search user behavior and log analysis; Search with Foundation Models Privacy, Fairness, Interpretability Fairness and accountability in ranking, recommendations and advertising; Explainability in web systems; Model and algorithm transparency; Interpretable models of individual and social behavior; Web search and data mining under privacy constraints; Fairness and interpretability in applications of web mining for social good. Social Networks Link prediction and community detection; Social network analysis and graph algorithms; Computational social science; Influence and viral marketing in social networks; Social sensing; Searching social and real-time content; Social network dynamics; Sampling, experiments, and evaluation in social networks; Social media analysis: blogs and friendship networks; Social network analysis, theories, models and applications; Social reputation and trust. Intelligent Assistants Voice search, conversational search, and dialog systems; Personal assistants, dialogue models, and conversational interaction; Task-driven search; Zero-query and implicit search. Crowdsourcing and Human Computation Collaborative search and question answering; Human-in-the-Loop and Collaborative Human-AI systems. Emerging and Creative Applications Web mining for social good; Systems and algorithms for urban applications such as smart cities/buildings/etc; Online education systems; Monitoring and prevention of epidemics; Social chatbots. Information Integrity Systems and algorithms for monitoring and detection of misinformation and fake news; Prevalence and virality of misinformation; Misinformation sources and origins; Source and content credibility; Detecting and combating spamming, trolling, aggression, dog whistles, and toxic online behaviors; Methods for detecting and mitigating low quality and offensive content, bullying and hate speech, and coordinated behavior; Polarization, extremism and radicalization; Echo chambers and filter bubbles. Foundation Models for Web Search and Data Mining Use of large language models (LLMs) and other foundation models for search and mining on the Web, including but not limited to the following tasks: Generative question answering; Indexing and query analysis; Pre-training and self-supervised learning for web-based tasks; Development of new user interfaces and user experiences; Support to information integrity.
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 16.8% over 10 years (2014–2023).

YearSubmittedAcceptedAccepted(%)
202369012317.8%
20225058015.8%
202160311218.6%
20206159114.8%
20195118416.4%
20185148416.3%
20175058015.8%
20163686718.2%
20152383916.4%
20143556418%

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