Automatically Identifying Groups Based on Content and Collective Behavioral Patterns of Group Members

Authors

  • Michelle Gregory Pacific Northwest National Laboratory
  • Dave Engel Pacific Northwest National Laboratory
  • Eric Bell Pacific Northwest National Laboratory
  • Andy Piatt Pacific Northwest National Laboratory
  • Scott Dowson Pacific Northwest National Laboratory
  • Andrew Cowell Pacific Northwest National Laboratory

Abstract

Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member’s interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.

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Published

2021-08-03

How to Cite

Gregory, M., Engel, D., Bell, E., Piatt, A., Dowson, S., & Cowell, A. (2021). Automatically Identifying Groups Based on Content and Collective Behavioral Patterns of Group Members. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 498-501. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14155