Wiki-Based Communities of Interest: Demographics and Outliers


  • Hiba Arnaout Max Planck Institute for Informatics
  • Simon Razniewski Max Planck Institute for Informatics
  • Jeff Z. Pan The University of Edinburgh



, Text categorization; topic recognition; demographic/gender/age identification


In this paper, we release data about demographic information and outliers of communities of interest. Identified from Wiki-based sources, mainly Wikidata, the data covers 7.5k communities, e.g., members of the White House Coronavirus Task Force, and 345k subjects, e.g., Deborah Birx. We describe the statistical inference methodology adopted to mine such data. We release subject-centric and group-centric datasets in JSON format, as well as a browsing interface. Finally, we forsee three areas where this dataset can be useful: in social sciences research, it provides a resource for demographic analyses; in web-scale collaborative encyclopedias, it serves as an edit recommender to fill knowledge gaps; and in web search, it offers lists of salient statements about queried subjects for higher user engagement. The dataset can be accessed at:




How to Cite

Arnaout, H., Razniewski, S., & Pan, J. Z. (2023). Wiki-Based Communities of Interest: Demographics and Outliers. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 990-996.