TY - GEN
T1 - DisCo-FEND
T2 - 25th International Conference on Web Information Systems Engineering, WISE 2024
AU - Jin, Weiqiang
AU - Wang, Ningwei
AU - Tao, Tao
AU - Jiang, Mengying
AU - Wang, Xiaotian
AU - Zhao, Biao
AU - Wu, Hao
AU - Duan, Haibin
AU - Yang, Guang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - With the rapid development of the Internet, traditional news channels are being supplanted, leading to an increased prevalence of fake news. Mainstream pre-trained language models (PLMs)-based fake news detection methods follow the ‘pre-training and fine-tuning’ paradigm, relying on full supervision and heavily dependent on large, high-quality datasets. In contrast to these methods, “pre-trained and prompt-tuning” offers more efficient learning, especially in data-scarce scenarios. Meanwhile, extensive analysis of social patterns reveals a tendency driven by user psychology and behavior: users often disseminate information that aligns with their pre-existing beliefs, thereby reinforcing and solidifying their convictions. This phenomenon is termed “social context veracity dissemination consistency”. Inspired by this phenomenon, we propose DisCo-FEND, A social context veracity Dissemination Consistency-guided case reasoning augmentation for the Fake News Detection (FEND) task. During model inference, we adopt a novel strategy that enhances reasoning by using multiple FEND cases. It leverages multiple news cases with higher dissemination consistency to refine predictions. Additionally, a high-quality label words acquisition approach and an adaptive weight allocation-based multi-label words mapping strategy improves the convergence and generalization of DisCo-FEND.
AB - With the rapid development of the Internet, traditional news channels are being supplanted, leading to an increased prevalence of fake news. Mainstream pre-trained language models (PLMs)-based fake news detection methods follow the ‘pre-training and fine-tuning’ paradigm, relying on full supervision and heavily dependent on large, high-quality datasets. In contrast to these methods, “pre-trained and prompt-tuning” offers more efficient learning, especially in data-scarce scenarios. Meanwhile, extensive analysis of social patterns reveals a tendency driven by user psychology and behavior: users often disseminate information that aligns with their pre-existing beliefs, thereby reinforcing and solidifying their convictions. This phenomenon is termed “social context veracity dissemination consistency”. Inspired by this phenomenon, we propose DisCo-FEND, A social context veracity Dissemination Consistency-guided case reasoning augmentation for the Fake News Detection (FEND) task. During model inference, we adopt a novel strategy that enhances reasoning by using multiple FEND cases. It leverages multiple news cases with higher dissemination consistency to refine predictions. Additionally, a high-quality label words acquisition approach and an adaptive weight allocation-based multi-label words mapping strategy improves the convergence and generalization of DisCo-FEND.
KW - Case-based reasoning augmentation
KW - Few-shot fake news detection
KW - Prompt-tuning
KW - Social Context Veracity Dissemination Consistency Network
KW - User spreading news engagement bias
UR - https://www.scopus.com/pages/publications/85211226028
U2 - 10.1007/978-981-96-0576-7_23
DO - 10.1007/978-981-96-0576-7_23
M3 - 会议稿件
AN - SCOPUS:85211226028
SN - 9789819605750
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 305
EP - 319
BT - Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings
A2 - Barhamgi, Mahmoud
A2 - Wang, Hua
A2 - Wang, Xin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 December 2024 through 5 December 2024
ER -