Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

TY - JOUR

T1 - Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

AU - Roy, Pradeep Kumar

AU - Ahmad, Zishan

AU - Singh, Jyoti Prakash

AU - Banerjee, Snehasish

N1 - © 2022 Informa UK Limited. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

PY - 2023

Y1 - 2023

N2 - 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. To this end, existing research has predominantly focused on finding the best answer, or identifying high-quality answers among the available responses. However, such scholarly efforts have not reduced the volume of low-quality answers on SQAs. 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. Data from Stack Exchange was used to carry out the investigation. Informed by the literature, 26 features were extracted. Thereafter, machine learning algorithms were implemented that could correctly identify 85% to 96% of low-quality answers. The key contribution of this research 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.

AB - 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. To this end, existing research has predominantly focused on finding the best answer, or identifying high-quality answers among the available responses. However, such scholarly efforts have not reduced the volume of low-quality answers on SQAs. 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. Data from Stack Exchange was used to carry out the investigation. Informed by the literature, 26 features were extracted. Thereafter, machine learning algorithms were implemented that could correctly identify 85% to 96% of low-quality answers. The key contribution of this research 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.

KW - answer quality

KW - data balancing

KW - low-quality answers

KW - social question answering

UR - https://www.tandfonline.com/doi/full/10.1080/03772063.2022.2048715

U2 - 10.1080/03772063.2022.2048715

DO - 10.1080/03772063.2022.2048715

M3 - Article

SN - 0377-2063

VL - 69

SP - 7933

EP - 7944

JO - IETE Journal of Research

JF - IETE Journal of Research

IS - 11

ER -