8 research outputs found
A Dynamic Feedback Framework for Studying Growth Policies in Open Online Collaboration Communities
The path to success: A study of user behaviour and success criteria in online communities
Maintaining online communities is vital in order to increase and retain their economic and social value. That is why community managers look to gauge the success of their communities by measuring a variety of user behaviour, such as member activity, turnover and interaction. However, such communities vary widely in their purpose, implementation and user demographics, and although many success indicators have been proposed in the literature, we will show that there is no one- ts-all approach to community success: Different success criteria depend on different user behaviour. To demonstrate this, we put together a set of user behaviour features, including many that have been used in the literature as indicators of success, and then we define and predict community success in three different types of online communities: Questions & Answers (Q&A), Healthcare and Emotional Support (Life & Health), and Encyclopaedic Knowledge Creation. The results show that it is feasible to relate community success to specific user behaviour with an accuracy of 0.67–0.93 F1 score and 0.77–1.0 AUC.This research has been conducted with the financial support of
Science Foundation Ireland (Grant Number SFI/12/RC/2289) and
with data provided by Stack Exchange, Boards.ie and Wikipedia.non-peer-reviewe
The path to success: A study of user behaviour and success criteria in online communities
Maintaining online communities is vital in order to increase and retain their economic and social value. That is why community managers look to gauge the success of their communities by measuring a variety of user behaviour, such as member activity, turnover and interaction. However, such communities vary widely in their purpose, implementation and user demographics, and although many success indicators have been proposed in the literature, we will show that there is no one- ts-all approach to community success: Different success criteria depend on different user behaviour. To demonstrate this, we put together a set of user behaviour features, including many that have been used in the literature as indicators of success, and then we define and predict community success in three different types of online communities: Questions & Answers (Q&A), Healthcare and Emotional Support (Life & Health), and Encyclopaedic Knowledge Creation. The results show that it is feasible to relate community success to specific user behaviour with an accuracy of 0.67–0.93 F1 score and 0.77–1.0 AUC.This research has been conducted with the financial support of
Science Foundation Ireland (Grant Number SFI/12/RC/2289) and
with data provided by Stack Exchange, Boards.ie and Wikipedia