Weeding out low-quality answers from social question & answer sites

What is it about?

Social Question Answering sites (SQAs) are online platforms that allow Internet users to ask questions, and obtain answers from others in the community. SQAs have been marred by the problem of low-quality answers. Worryingly, answer quality on SQAs have been reported to be following a downward trajectory in recent years. Therefore, this research uses machine learning to weed out low-quality answers as soon as they are posted on SQAs.

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Why is it important?

Existing research has predominantly focused on finding the best answer, or identifying high-quality answers on SQAs. However, such scholarly efforts have not reduced the volume of low-quality answers. Therefore, the goal of this research is to extract features in order to weed out low-quality answers as soon as they are posted on SQAs. The key contribution is the development of a system to detect subpar answers on the fly at the time of posting. It is intended to be used as an early warning system that warns users about answer quality at the point of posting.