- About
- Events
- Calendar
- Graduation Information
- Cornell Tech Colloquium
- Student Colloquium
- Student Recognition
- 2020 Celebratory Event
- BOOM
- CS Colloquium
- Conway-Walker Lecture Series
- Salton Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University High School Programming Contest
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- Research Night
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
- Admissions
- Current Students
- Field of Computer Science Ph.D. Student Handbook
- Ph.D. Requirements
- Business Card Policy
- Computer Science Graduate Office Hours
- Cornell Tech
- Curricular Practical Training
- Exam Scheduling Guidelines
- Fellowship Opportunities
- Field A Exam Summary Form
- Graduate School Forms
- Ph.D. Student Financial Support
- Special Committee Selection
- The Outside Minor Requirement
- Travel Funding Opportunities
- Diversity and Inclusion
- Graduation Information
- CS Graduate Minor
- Outreach Opportunities
- Parental Accommodation Policy
- Special Masters
- Student Groups
- Student Spotlights
- Contact PhD Office
Title: Trajectories of Blocked Community Members: Redemption, Recidivism and Departure
Abstract: Community norm violations can impair constructive communication and collaboration online. As a defense mechanism, community moderators often address such transgressions by temporarily blocking the perpetrator. Such actions, however, come with the cost of potentially alienating community members. Given this tradeoff, it is essential to understand to what extent, and in which situations, this common moderation practice is effective in reinforcing community rules.
In this work, we introduce a computational framework for studying the future behavior of blocked users on Wikipedia. After their block expires, they can take several distinct paths: they can reform and adhere to the rules, but they can also recidivate, or straight-out abandon the community. We reveal that these trajectories are tied to factors rooted both in the characteristics of the blocked individual and in whether they perceived the block to be fair and justified. Based on these insights, we formulate a series of prediction tasks aiming to determine which of these paths a user is likely to take after being blocked for their first offense, and demonstrate the feasibility of these new tasks. Overall, this work builds towards a more nuanced approach to moderation by highlighting the tradeoffs that are in play.