Predicting Response Quantity from Linguistic Characteristics of Questions on Academic Social Q&A Sites

Lei Li, Anrunze Li, Xue Song, Xinran Li, Kun Huang, Edwin Mouda Ye

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Academic social Q&A websites have a lower response quantity than other types of social Q&A. To help academic social Q&A platforms implement mechanisms to improve the quantities of responses to questions that are rarely answered and to predict these quantities, this study uses 93 features representing the linguistic characteristics of academic questions, and compares several methods of prediction to determine the one that delivers the best performance. It also identifies the most useful feature set for such predictions.

Original languageEnglish
Title of host publicationDigital Libraries at Times of Massive Societal Transition - 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Proceedings
EditorsEmi Ishita, Natalie Lee Pang, Lihong Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages399-406
Number of pages8
ISBN (Print)9783030644512
DOIs
StatePublished - 2020
Externally publishedYes
Event22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 - Kyoto, Japan
Duration: 30 Nov 20201 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12504 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020
Country/TerritoryJapan
CityKyoto
Period30/11/201/12/20

Keywords

  • Academic questions
  • Academic social Q&A
  • Linguistic characteristics
  • Response quantity prediction

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