Matches in ScholarlyData for { ?s ?p One of the important targets of community-based question answering (CQA)services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and evenincrease the number of active answerers, that is the users who provide answersto open questions. The reasoning is that they are the engine behind satisfiedaskers, 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 twoanswers and then leave.In this work we try to detect answerers that are about to quit, a task known aschurn prediction, but unlike prior work, we focus on new users. To address thetask of churn prediction in new users, we extract a variety of features to modelthe behavior of Yahoo! Answers 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 statisticallysignificant signal for discriminating between users who are likely to churn andthose who are not. A detailed feature analysis shows that the two most importantsignals are the total number of answers given by the user, closely related tothe motivation of the user, and attributes related to the amount of recognitiongiven to the user, measured in counts of best answers, thumbs up and positiveresponses by the asker.. }
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- 11 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 evenincrease the number of active answerers, that is the users who provide answersto open questions. The reasoning is that they are the engine behind satisfiedaskers, 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 twoanswers and then leave.In this work we try to detect answerers that are about to quit, a task known aschurn prediction, but unlike prior work, we focus on new users. To address thetask of churn prediction in new users, we extract a variety of features to modelthe behavior of Yahoo! Answers 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 statisticallysignificant signal for discriminating between users who are likely to churn andthose who are not. A detailed feature analysis shows that the two most importantsignals are the total number of answers given by the user, closely related tothe motivation of the user, and attributes related to the amount of recognitiongiven to the user, measured in counts of best answers, thumbs up and positiveresponses by the asker.".