Name
Abstract | ||
|---|---|---|
One of the important targets of community-based question answering (CQA) services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and even increase the number of active answerers, that is the users who provide answers to open questions. The reasoning is that they are the engine behind satisfied askers, which is the overall goal behind CQA. Yet, this task is not an easy one. Indeed, our empirical observation shows that many users provide just one or two answers and then leave. In this work we try to detect answerers that are about to quit, a task known as churn prediction, but unlike prior work, we focus on new users. To address the task of churn prediction in new users, we extract a variety of features to model the behavior of \YA{} users over the first week of their activity, including personal information, rate of activity, and social interaction with other users. Several classifiers trained on the data show that there is a statistically significant signal for discriminating between users who are likely to churn and those who are not. A detailed feature analysis shows that the two most important signals are the total number of answers given by the user, closely related to the motivation of the user, and attributes related to the amount of recognition given to the user, measured in counts of best answers, thumbs up and positive responses by the asker. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2187980.2188207 | WWW (Companion Volume) |
Keywords | Field | DocType |
community-based question answering,total number,best answer,important signal,new user,active answerers,baidu zhidao,prior work,important target,churn prediction,social interaction,feature analysis,satisfiability,question answering,statistical significance,information rate | Social relation,Data mining,World Wide Web,Question answering,Computer science,Personally identifiable information,Pattern recognition (psychology) | Conference |
Citations | PageRank | References |
47 | 1.88 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gideon Dror | 1 | 1761 | 104.44 |
| Dan Pelleg | 2 | 1552 | 107.09 |
| Oleg Rokhlenko | 3 | 250 | 17.03 |
| Idan Szpektor | 4 | 841 | 59.44 |