Wiki Workshop 2020

A forum bringing together researchers exploring all aspects of Wikipedia, Wikidata, and other Wikimedia projects. Held at The Web Conference 2020 in Taipei, Taiwan, 21 April 2020.

  • Dec. 4, 2019: Workshop date announced: Tuesday, 21 April 2020.
  • Nov. 15, 2019: Wiki Workshop 2020 webpage online.

We will have a series of invited talks by academia and industry experts, as well as a combination of lightning talks and a poster session for the accepted papers.

Details to be determined.

Workshop date: Tuesday, 21 April 2020

If authors want paper to appear in proceedings:

  • Submission deadline: 17 January 2020
  • Author feedback: 3 February 2020
  • Camera-ready version due: 17 February 2020

If authors do not want paper to appear in proceedings:

  • Submission deadline: 21 February 2020
  • Author feedback: 6 March 2020
Note: If you need a visa to travel to Taiwan and your application for the visa depends on your workshop paper being accepted, we would advise you to submit your workshop paper for the 17 January deadline. (You could still opt for not having your paper included in the proceedings.)

Wikipedia is one of the most popular sites on the Web, a main source of knowledge for a large fraction of Internet users, and one of the very few projects that make not only their content but also many activity logs available to the public. Furthermore, other Wikimedia projects, such as Wikidata and Wikimedia Commons, have been created to share other types of knowledge with the world for free. For a variety of reasons (quality and quantity of content, reach in many languages, process of content production, availability of data, etc.) such projects have become important objects of study for researchers across many subfields of the computational and social sciences, such as social network analysis, artificial intelligence, linguistics, natural language processing, social psychology, education, anthropology, political science, human–computer interaction, and cognitive science.

The goal of this workshop is to bring together researchers exploring all aspects of Wikimedia projects such as Wikipedia, Wikidata, and Commons. With members of the Wikimedia Foundation's Research team on the organizing committee and with the experience of successful workshops in 2015, 2016, 2017, 2018, and 2019, we aim to continue facilitating a direct pathway for exchanging ideas between the organization that coordinates Wikimedia projects and the researchers interested in studying them.

Topics of interest include, but are not limited to

  • new technologies and initiatives to grow content, quality, diversity, and participation across Wikimedia projects
  • use of bots, algorithms, and crowdsourcing strategies to curate, source, or verify content and structured data
  • bias in content and gaps of knowledge
  • diversity of Wikimedia editors and users
  • detection of low-quality, promotional, or fake content, as well as fake accounts (e.g., sock puppets)
  • questions related to community health (e.g., sentiment analysis, harassment detection)
  • understanding editor motivations, engagement models, and incentives
  • Wikimedia consumer motivations and their needs: readers, researchers, tool/API developers
  • innovative uses of Wikipedia and other Wikimedia projects for AI and NLP applications
  • consensus-finding and conflict resolution on editorial issues
  • participation in discussions and their dynamics
  • dynamics of content reuse across projects and the impact of policies and community norms on reuse
  • privacy
  • collaborative content creation (unstructured, semi-structured, or structured)
  • innovative uses of Wikimedia projects' content and consumption patterns as sensors for real-world events, culture, etc.
  • open-source research code, datasets, and tools to support research on Wikimedia contents and communities

Papers should be 1 to 8 pages long and will be published on the workshop webpage and optionally (depending on the authors' choice) in the workshop proceedings. The review process will be single-blind (as opposed to double-blind), i.e., authors should include their names and affiliations in their submissions. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session.

We explicitly encourage the submission of preliminary work in the form of extended abstracts (1 or 2 pages).

Papers should be 1 to 8 pages long. We explicitly encourage the submission of preliminary work in the form of extended abstracts (1 or 2 pages). No need to anonymize your submissions.

For submission dates, see above.

  • Giovanni Colavizza, Umiversity of Amsterdam
  • Besnik Fetahu, L3S Hannover
  • Kristina Gligoric, EPFL
  • Manoel Horta Ribeiro, EPFL
  • Isaac Johnson, Wikimedia Foundation
  • Markus Kroetzsch, University of Dresden
  • Florian Lemmerich, RWTH Aachen University
  • Jonathan Morgan, Wikimedia Foundation
  • Daniela Paolotti, ISI Foundation
  • Tiziano Piccardi, EPFL
  • Diego Saez-Trumper, Wikimedia Foundation
  • Michele Tizzoni, ISI Foundation
  • Morten Warncke-Wang, Wikimedia Foundation
  • Ramtin Yazdanian, EPFL
  • Amy Zhang, MIT

Miriam Redi

Miriam is a Research Scientist at the Wikimedia Foundation and Visiting Research Fellow at King's College London. Formerly, she worked as a Research Scientist at Yahoo! Labs in Barcelona and Nokia Bell Labs in Cambridge. She received her PhD from EURECOM, Sophia Antipolis. She conducts research in social multimedia computing, working on fair, interpretable, multimodal machine learning solutions to improve knowledge equity.

Leila Zia

Leila is a senior research scientist at the Wikimedia Foundation. Her current research interests are on understanding Wikipedia's readers, quantifying and addressing the gaps of knowledge in Wikipedia and Wikidata, and understanding and improving diversity in Wikipedia. She holds a PhD in management science and engineering from Stanford University.

Robert West

Bob is an assistant professor of Computer Science at EPFL, where he heads the Data Science Lab. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. He holds a PhD in computer science from Stanford University.

Please direct your questions to wikiworkshopgooglegroupscom.