Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection

Hu, Bing; Sheng, Qiang; Cao, Juan; Zhu, Yinian; Wang, Danding; Wang, Zhengjia; Jin, Zhiwei

doi:10.18653/v1/2023.acl-industry.13

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent methods (Zhang et al 2021;Kaliyar, Goswami, and Narang 2021;Mosallanezhad et al 2022;Hu et al 2023) generally exploit pre-trained small language models (SLMs) 1 like BERT (Devlin et al 2019) and RoBERTa (Liu et al 2019) to understand news content and provide fundamental representation, plus optional social contexts (Shu et al 2019;Cui et al 2022), knowledge bases (Popat et al 2018;Hu et al 2022b), or news environment (Sheng et al 2022) as supplements. SLMs do bring improvements, but their knowledge and capability limitations also compromise further enhancement of fake news detectors.…”

Section: Realmentioning

confidence: 99%

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

Hu,

Sheng,

Cao

et al. 2024

AAAI

View full text Add to dashboard Cite

Detecting fake news requires both a delicate sense of diverse clues and a profound understanding of the real-world background, which remains challenging for detectors based on small language models (SLMs) due to their knowledge and capability limitations. Recent advances in large language models (LLMs) have shown remarkable performance in various tasks, but whether and how LLMs could help with fake news detection remains underexplored. In this paper, we investigate the potential of LLMs in fake news detection. First, we conduct an empirical study and find that a sophisticated LLM such as GPT 3.5 could generally expose fake news and provide desirable multi-perspective rationales but still underperforms the basic SLM, fine-tuned BERT. Our subsequent analysis attributes such a gap to the LLM's inability to select and integrate rationales properly to conclude. Based on these findings, we propose that current LLMs may not substitute fine-tuned SLMs in fake news detection but can be a good advisor for SLMs by providing multi-perspective instructive rationales. To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales. We further derive a rationale-free version of ARG by distillation, namely ARG-D, which services cost-sensitive scenarios without inquiring LLMs. Experiments on two real-world datasets demonstrate that ARG and ARG-D outperform three types of baseline methods, including SLM-based, LLM-based, and combinations of small and large language models.

show abstract

Section: Realmentioning

confidence: 99%

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

Hu,

Sheng,

Cao

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

Combating misinformation in the age of LLMs: Opportunities and challenges

Chen,

Shu

2024

AI Magazine

View full text Add to dashboard Cite

Misinformation such as fake news and rumors is a serious threat for information ecosystems and public trust. The emergence of large language models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double‐edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emerging question is: can we utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM‐generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions, respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM‐generated misinformation.

show abstract

Combating Online Misinformation Videos: Characterization, Detection, and Future Directions

Yuyan¹,

Shen²,

Cao³

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

View full text Add to dashboard Cite

Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection

Cited by 4 publications

References 0 publications

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

Combating misinformation in the age of LLMs: Opportunities and challenges

Combating Online Misinformation Videos: Characterization, Detection, and Future Directions

Contact Info

Product

Resources

About