WSDM (pronounced “wisdom”) is one of the premier conferences on web-inspired research involving search and data mining. The 17th ACM International WSDM Conference will take place in Mérida, México (in-person conference with virtual elements). The conference will be held at the Centro Internacional de Congresos Yucatán from March 4th to March 8th 2024.

WSDM is a highly selective conference that includes invited talks, as well as refereed full papers. WSDM publishes original, high-quality papers related to search and data mining on the Web and the Social Web, with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance.

List of Topics

WSDM proceedings are published in the ACM Digital Library. We invite papers in the areas below, with example topics listed for each area.

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
Geo-location data analysis; Mobility and behavior data mining; Location-based social networks; User activity modeling and exploitation.
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.

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
Mental health and well-being support systems; 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.

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, and toxic online behaviors; Methods for detecting and mitigating low quality and offensive content, bullying and hate speech; Polarization, extremism and radicalization; Echo chambers and filter bubbles.

Foundation Models
Use of large-language models and other foundation models for web search and data mining, including but not limited to the following tasks: 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.