Abstract
Researchers studying user behaviors in online communities often conduct analyses of user interaction data recorded in system logs e.g., an edit in Wikipedia. Such analysis relies on collating interactions by a unique identifier such as a user ID. However, if users can contribute without being logged-in (i.e., anonymously) analysis of interaction data omit part of a user’s experience. Problematically, anonymous traces are unlikely to be randomly distributed, so their omission can change statistical conclusions, with implications for both research and practice. To understand the impacts on conclusions of leaving out anonymous traces, we conducted an analysis of system logs from two online citizen science projects. Attributing anonymous traces with user IDs, we found that (1) many users contribute anonymously, though with varied patterns; and (2) attributing anonymous traces diminishes empirical evidence used to support theory and change the results of system algorithms. These results suggest anonymous traces have implications for research on user behaviors and the practices associated with using such data to tailor user experiences in online communities.
| Original language | English (US) |
|---|---|
| Article number | 77 |
| Journal | Proceedings of the ACM on Human-Computer Interaction |
| Volume | 2 |
| Issue number | CSCW |
| DOIs | |
| State | Published - Nov 1 2018 |
Keywords
- Anonymity
- Citizen science
- Online communities
- User behavior
- Zooniverse
ASJC Scopus subject areas
- Computer Networks and Communications
- Human-Computer Interaction
- Social Sciences (miscellaneous)