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research-article

Improving answer quality using image-text coherence on social Q&A sites

Published: 09 July 2024 Publication History

Abstract

There has been a significant rise in the use of social Q&A to get answers to a variety of queries. One common problem faced by most social Q&A is how to help unskillful answerers construct well-received answers. Prior studies in answer quality assessment usually focus on ranking candidate answers for the sake of askers but show little value for the answerers. Moreover, existing work employs textural aspects and image quantity to predict answer quality, but semantic information inherent in answer images is rarely considered. To bridge the research gap, we designed an artifact, answer advisor (AA), to help answerers produce well-received answers. Our AA uses an image-text coherence measure that is obtained by integrating topic modeling with a deep learning approach. On a real-world dataset, the proposed measure can reduce the prediction error of answer popularity before the answer is actually posted on the Q&A site by 38.12%.

Highlights

Many social Q&A users are unskillful answerers.
An automatic answer advisor for writing well-received answers is necessary.
High image-text coherence of answer content can increase answer popularity.
A deep learning approach is developed to measure the image-text coherence.
The image-text coherence measure can significantly enhance the answer advisor.

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Information & Contributors

Information

Published In

cover image Decision Support Systems
Decision Support Systems  Volume 180, Issue C
May 2024
254 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 09 July 2024

Author Tags

  1. Social Q&A
  2. Answerer assistance
  3. Image topic modeling
  4. Topic coherence
  5. Deep learning

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