TY - GEN
T1 - The path to success
T2 - 16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
AU - Aumayr, Erik
AU - Hayes, Conor
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/8/23
Y1 - 2017/8/23
N2 - 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-fits-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.
AB - 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-fits-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.
KW - Community success
KW - Online communities
KW - User behaviour
UR - https://www.scopus.com/pages/publications/85031006523
U2 - 10.1145/3106426.3106469
DO - 10.1145/3106426.3106469
M3 - Conference Publication
AN - SCOPUS:85031006523
T3 - Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
SP - 219
EP - 226
BT - Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
PB - Association for Computing Machinery, Inc
Y2 - 23 August 2017 through 26 August 2017
ER -