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Question popularity analysis and prediction in community question answering services.
Ting Liu1, Wei-Nan Zhang1, Liujuan Cao2
1Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin City, Heilongjiang, China.
Plos One
|May 20, 2014
Summary
Predicting question popularity in Community Question Answering (CQA) services helps understand user interest. This study develops a machine learning model to identify popular questions, enhancing user experience and community growth.
Area of Science:
- Information Science
- Computer Science
- Social Media Analysis
Background:
- Community Question Answering (CQA) services are vital online knowledge resources.
- Existing research focuses on question/answer retrieval, neglecting question popularity.
- Question popularity indicates user attention and interest, crucial for community development.
Purpose of the Study:
- To investigate and predict question popularity in CQA services.
- To identify key factors influencing question popularity.
- To improve user experience by capturing user interests.
Main Methods:
- Statistical analysis to explore factors impacting question popularity.
- Development of a supervised machine learning model for popularity prediction.
- Utilizing the Yahoo! Answers dataset for empirical evaluation.
Main Results:
- Identified significant factors influencing question popularity.
- The proposed machine learning approach effectively distinguishes popular from unpopular questions.
- Demonstrated the model's efficacy on a large-scale CQA dataset.
Conclusions:
- Predicting question popularity is feasible and beneficial for CQA platforms.
- The developed model offers a practical solution for identifying engaging content.
- This work contributes to enhancing user engagement and community vitality in CQA.
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