A novel hybrid CNN-LSTM approach for assessing StackOverflow post quality

Zeeshan Anwar, Hammad Afzal, Ali Ahsan, Naima Iltaf, Ayesha Maqbool

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Maintaining the content quality on social media Q&A platforms is pivotal for user attraction and retention. Automating post quality assessment offers benefits such as reduced moderator workload, amplified community impact, enhanced expert user recognition, and importance to expert feedback. While existing approaches for post quality mainly employ binary classification, they often lack optimal feature selection. Our research introduces an automated system that categorizes features into textual, readability, format, and community dimensions. This system integrates 20 features belonging to the aforementioned categories, with a hybrid convolutional neural network–long short-term memory deep learning model for multi-class classification. Evaluation against baseline models and state-of-the-art methods demonstrates our system’s superiority, achieving a remarkable 21–23% accuracy enhancement. Furthermore, our system produced better results in terms of other metrics such as precision, recall, and F1 score.

Original languageEnglish
Article number20230057
JournalJournal of Intelligent Systems
Volume32
Issue number1
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

  • community question answering
  • crowd sourcing
  • deep learning
  • quality assessment

Fingerprint

Dive into the research topics of 'A novel hybrid CNN-LSTM approach for assessing StackOverflow post quality'. Together they form a unique fingerprint.

Cite this