@inproceedings{7ca2e1a804e2469c92d9fa5c245920fb,
title = "The phantom steering effect in QA websites",
abstract = "Badges are commonly used in online platforms as incentives for promoting contributions. It is widely accepted that badges 'steer' people's behavior toward increasing their rate of contributions before obtaining the badge. This paper provides a new probabilistic model of user behavior in the presence of badges. By applying the model to data from thousands of users on the QA site Stack Overflow, we find that steering is not as widely applicable as was previously understood. Rather, the majority of users remain apathetic toward badges, while still providing a substantial number of contributions to the site. An interesting statistical phenomenon, termed 'Phantom Steering, ' accounts for the interaction data of these users and this may have contributed to some previous conclusions about steering. Our results suggest that a small population, approximately 20%, of users respond to the badge incentives. Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community. We argue that while badges might contribute toward a suite of effective rewards in an online system, research into other aspects of reward systems, such as Stack Overflow's reputation points, should become a focus of the community.",
keywords = "Amortized inference, Goal-gradient hypothesis, Steering, Virtual badges",
author = "Nicholas Hoernle and Gregory Kehne and Procaccia, {Ariel D.} and Kobi Gal",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Data Mining, ICDM 2020 ; Conference date: 17-11-2020 Through 20-11-2020",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/ICDM50108.2020.00026",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "172--181",
editor = "Claudia Plant and Haixun Wang and Alfredo Cuzzocrea and Carlo Zaniolo and Xindong Wu",
booktitle = "Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020",
address = "United States",
}