In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Dual-channel multi-level contrastive learning stance detection based on hybrid AutoEncoder

Cao Junhao
Zhu Xiaofei
Cheng Jiawei
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0424

Abstract

Stance Detection aims to identify the author's attitudes or positions (agree, disagree, neutral) towards a specific text. Existing methods based solely on textual information exhibit limitations, particularly in cross-domain scenarios. To address this, research was conducted on leveraging social relationship information to enhance stance detection. Social relationship information was extracted from comments and replies and constructed into an inductive social relationship graph. A hybrid autoencoder stance detection framework, combining Autoencoder (AE) and Variational Autoencoder(VAE) , was proposed to precisely model the social relationship features of authors and improve stance recognition performance. To further learn robust social relationship features, a dual-channel multi-level contrastive learning method was designed. This method enhances the model's ability to capture social relationship by contrasting features within the hybrid autoencoder and across different social relationship graphs. Experiments on the DEBAGREEMENT dataset for in-domain and cross-domain tasks demonstrated that the proposed framework outperformed multiple baseline models. The findings validate the significance of social relationship information in stance detection tasks and provide new approaches and insights for future research.

Foundation Support

国家自然科学基金资助项目(62472059)
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1672)
重庆英才计划项目(CSTC2024YCJH-BGZXM0022)
重庆市教育委员会科学技术研究计划重大项目(KJZD-M202201102)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0424
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-03-06] Accepted Paper

Cite This Article

曹均皓, 朱小飞, 程佳玮. 基于混合自编码器的双通道多层次对比学习立场检测 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0424. (Cao Junhao, Zhu Xiaofei, Cheng Jiawei. Dual-channel multi-level contrastive learning stance detection based on hybrid AutoEncoder [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0424. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)