Thanks for Stopping By: A Study of "Thanks" Usage on Wikimedia

03/08/2019
by   Swati Goel, et al.
0

The Thanks feature on Wikipedia, also known as "Thanks", is a tool with which editors can quickly and easily send one other positive feedback. The aim of this project is to better understand this feature: its scope, the characteristics of a typical "Thanks" interaction, and the effects of receiving a thank on individual editors. We study the motivational impacts of "Thanks" because maintaining editor engagement is a central problem for crowdsourced repositories of knowledge such as Wikimedia. Our main findings are that most editors have not been exposed to the Thanks feature (meaning they have never given nor received a thank), thanks are typically sent upwards (from less experienced to more experienced editors), and receiving a thank is correlated with having high levels of editor engagement. Though the prevalence of "Thanks" usage varies by editor experience, the impact of receiving a thank seems mostly consistent for all users. We empirically demonstrate that receiving a thank has a strong positive effect on short-term editor activity across the board and provide preliminary evidence that thanks could compound to have long-term effects as well.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/10/2023

Ranking with Long-Term Constraints

The feedback that users provide through their choices (e.g., clicks, pur...
research
08/06/2022

Long-Term Mentoring for Computer Science Researchers

Early in the pandemic, we – leaders in the research areas of programming...
research
08/01/2020

Time Series Analysis and Correlation of Subway Turnstile Usage and COVID-19 Prevalence in New York City

In this paper, we show a strong correlation between turnstile usage data...
research
03/17/2023

Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out

With the growing popularity of intelligent assistants (IAs), evaluating ...
research
11/24/2022

Learning to Take a Break: Sustainable Optimization of Long-Term User Engagement

Optimizing user engagement is a key goal for modern recommendation syste...

Please sign up or login with your details

Forgot password? Click here to reset