Loading...

Multi-view approach to suggest moderation actions in community question answering sites

Annamoradnejad, I ; Sharif University of Technology | 2022

88 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.ins.2022.03.085
  3. Publisher: Elsevier Inc , 2022
  4. Abstract:
  5. With thousands of new questions posted every day on popular Q&A websites, there is a need for automated and accurate software solutions to replace manual moderation. In this paper, we address the critical drawbacks of crowdsourcing moderation actions in Q&A communities and demonstrate the ability to automate moderation using the latest machine learning models. From a technical point, we propose a multi-view approach that generates three distinct feature groups that examine a question from three different perspectives: 1) question-related features extracted using a BERT-based regression model; 2) context-related features extracted using a named-entity-recognition model; and 3) general lexical features derived using statistical and analytical methods. As a last step, we train a gradient boosting classifier to predict a moderation action. For evaluation purposes, we created a new dataset consisting of 60,000 Stack Overflow questions classified into three choices of moderation actions. Based on cross-validation on the novel dataset, our approach reaches 95.6% accuracy as a multiclass task and outperforms all state-of-the-art and previously-published models. Our results clearly demonstrate the high influence of our feature generation components on the overall success of the classifier. © 2022 Elsevier Inc
  6. Keywords:
  7. Automatic moderation ; Decision support system ; Multi-view learning ; User-generated content ; Artificial intelligence ; Learning systems ; Regression analysis ; Community question answering ; Feature groups ; Machine learning models ; Multi-views ; Software solution ; Stack overflow ; User-generated ; Decision support systems
  8. Source: Information Sciences ; Volume 600 , 2022 , Pages 144-154 ; 00200255 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0020025522003127