Exploiting user comments for early detection of fake news prior to users' commenting

doi:10.1007/s11704-024-40674-6

	Exploiting user comments for early detection of fake news prior to users' commenting
	Nan, Qiong 1,2; Sheng, Qiang 1; Cao, Juan 1,2; Zhu, Yongchun 1; Wang, Danding 1; Yang, Guang 3; Li, Jintao 1
	2025-10-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	19 期号:10 页码:13
摘要	Both accuracy and timeliness are key factors in detecting fake news on social media. However, most existing methods encounter an accuracy-timeliness dilemma: Content-only methods guarantee timeliness but perform moderately because of limited available information, while social context-based ones generally perform better but inevitably lead to latency because of social context accumulation needs. To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e.g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts. This requires the model to (1) sufficiently learn helpful knowledge from social contexts, and (2) be well compatible with situations that social contexts are available or not. To achieve this goal, we propose to absorb and parameterize useful knowledge from comments in historical news and then inject it into a content-only detection model. Specifically, we design the Comments ASsisted FakENews Detection method (CAS-FEND), which transfers useful knowledge from a comment-aware teacher model to a content-only student model and detects newly emerging news with the student model. Experiments show that the CAS-FEND student model outperforms all content-only methods and even comment-aware ones with 1/4 comments as inputs, demonstrating its superiority for early detection.
关键词	fake news detection knowledge distillation early detection
DOI	10.1007/s11704-024-40674-6
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFC3302102] ; National Natural Science Foundation of China[62406310] ; National Natural Science Foundation of China[62203425] ; Postdoctoral Fellowship Program of CPSF[GZC20232738] ; China Postdoctoral Science Foundation[2022TQ0344] ; China Postdoctoral Science Foundation[2024M763336] ; International Postdoctoral Exchange Fellowship Program by Office of China Postdoc Council[YJ20220198] ; Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China[SKLMCC2022KF001]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001407831600005
出版者	HIGHER EDUCATION PRESS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40767
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cao, Juan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Zhongguancun Lab, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Nan, Qiong,Sheng, Qiang,Cao, Juan,et al. Exploiting user comments for early detection of fake news prior to users' commenting[J]. FRONTIERS OF COMPUTER SCIENCE,2025,19(10):13.
APA	Nan, Qiong.,Sheng, Qiang.,Cao, Juan.,Zhu, Yongchun.,Wang, Danding.,...&Li, Jintao.(2025).Exploiting user comments for early detection of fake news prior to users' commenting.FRONTIERS OF COMPUTER SCIENCE,19(10),13.
MLA	Nan, Qiong,et al."Exploiting user comments for early detection of fake news prior to users' commenting".FRONTIERS OF COMPUTER SCIENCE 19.10(2025):13.