Value of online Question Answering (QandA) communities is driven by the question-answering behaviour of its members. Finding the questions that members are willing to answer is therefore vital to the effcient operation of such communities. In this paper, we aim to identify the parameters that cor- relate with such behaviours. We train different models and construct effective predictions using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success.
|Title of host publication||WWW'15 Companion - Proceedings of the 24th International Conference on World Wide Web|
|Place of Publication||New York, NY (US)|
|Number of pages||2|
|Publication status||Published - 18 May 2015|
|Event||24th International Conference on World Wide Web - Florence, Italy|
Duration: 18 May 2015 → 22 May 2015
|Conference||24th International Conference on World Wide Web|
|Abbreviated title||WWW 2015|
|Period||18/05/15 → 22/05/15|
Bibliographical noteFunding: EC-FP7 project DecarboNet (grant number 265454).
- online communities
- social media
- social QandA platforms
- user behaviour