Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stack Overflow is a popular community-based Qaamp;A bsite that caters to technical needs of software developers. As of February 2015 -- Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study,  propose a two stage hybrid approach -- DelPredictor -- which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features.  evaluate DelPredictor on 5 years (2008 -- 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.
['Xin Xia', 'David Lo', 'Denzil Correa', 'Ashish Sureka', 'Emad Shihab']
I am writing a research paper on a idea It Takes Two to Tango: Deleted Stack Overflow Question Prediction with Text and Meta Features
[{"from": "human", "value": "I am writing a research paper on a idea It Takes Two to Tango: Deleted Stack Overflow Question Prediction with Text and Meta Features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stack Overflow is a popular community-based Qaamp;A bsite that caters to technical needs of software developers. As of February 2015 -- Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study,  propose a two stage hybrid approach -- DelPredictor -- which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features.  evaluate DelPredictor on 5 years (2008 -- 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively."}]