Survey on Text-Level Stance Detection

doi:10.19678/j.issn.1000-3428.0252679

Abstract

Abstract: With the popularization and development of the Internet, the massive volume of user-generated comments on trending topics and their widespread dissemination have profoundly influenced the progression and development of real-world events. Consequently, mining public stances and attitudes toward trending topics has significant practical value in domains such as online public opinion monitoring and social security governance. Stance detection technology aims to identify user attitudes toward specific targets from user-generated texts. Although numerous studies have proposed diverse task scenarios and technical methodologies, a unified classification framework for stance detection tasks remains elusive. This paper presents a comprehensive review of stance detection tasks from two dimensions, task scenarios and technical methodologies, systematically organizing the current research landscape and development trends. From the task scenario perspective, stance detection is classified into three paradigms, target-specific, target transfer, and target generalization, highlighting the evolution of the field from domain-specific applications to broader adaptability. From the methodological perspective, stance detection approaches are categorized into three primary classes, namely model-based engineering, knowledge-driven engineering, and data-centric engineering, and the strengths and limitations of each class are analyzed. Additionally, statistical and experimental analyses of publicly available resources are presented across multiple dimensions to reveal the key characteristics and developmental trajectories of the benchmark datasets. The paper concludes with a summary and prospective research directions and persistent challenges are outlined.

Key words: stance, stance detection, task scenario, technical approach, data resource

摘要： 随着互联网的普及和发展，海量用户对热点话题的评论与传播，深刻影响着现实世界中事件的进程与发展。因此，挖掘公众对热点话题的立场与态度，对于网络舆情监测、社会安全治理等领域具有重要的现实意义。立场检测技术旨在从用户生成的文本中识别其对特定对象的态度。尽管已有许多研究提出了不同的任务场景和技术方法，但针对立场检测任务，仍未形成统一的分类标准。从任务场景和技术方法2个维度对立场检测任务进行综述，系统梳理了该领域的研究现状与发展趋势。从任务场景的角度，将立场检测任务划分为面向特定对象、对象迁移和对象泛化3种场景，突出了研究从特定领域向更广泛应用逐步演进的趋势。从技术方法的角度，将立场检测方法归纳为基于模型工程、知识工程和数据工程的三大类，并分析了各类方法的优势与局限性。此外，还从多个维度对公开的数据资源进行了统计与实验分析，揭示了立场检测数据集的关键特征及发展趋势。最后，对全文进行了总结，并展望了未来的发展方向及面临的挑战。

关键词: 立场, 立场检测, 任务场景, 技术方法, 数据资源

CLC Number:

TP18

FAN Qinlong, SUN Yepeng, LU Jicang, ZHU Taojie, LIU Yilin. Survey on Text-Level Stance Detection[J]. Computer Engineering, 2026, 52(11): 27-42.

范钦龙, 孙业鹏, 卢记仓, 祝涛杰, 刘伊琳. 文本级立场检测综述[J]. 计算机工程, 2026, 52(11): 27-42.

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