Journal ArticleDOI
Fake news detection via knowledgeable prompt learning
Reads0
Chats0
TLDR
Zhang et al. as mentioned in this paper proposed knowledgeable prompt learning (KPL) for fake news detection, which incorporates external knowledge into the prompt representation, making the representation more expressive to predict the verbal words.Abstract:
The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning. • We leverage a pretrained language model by prompt learning for fake news detection. • We propose K nowledgeable P rompt L earning, injecting knowledge into prompt learning. • Experiments demonstrate the effectiveness of pretrained information and external knowledge. read more
Citations
More filters
Journal ArticleDOI
A Scoping Review of the Relationship of Big Data Analytics with Context-Based Fake News Detection on Digital Media in Data Age
TL;DR: In this paper , the authors identify the relationship between big data analytics with context-based news detection on digital media in the data age, and explore the challenges for constructing quality big data to detect misinformation on social media.
Proceedings ArticleDOI
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
TL;DR: MetaAdapt as mentioned in this paper leverages limited target examples to provide feedback and guide the knowledge transfer from the source to the target domain (i.e., learn to adapt) for early-stage misinformation detection.
Proceedings ArticleDOI
A Novel Framework for Fake News Detection using Double Layer BI-LSTM
TL;DR: In this article , a new framework has been proposed that utilizes Porter Stemmer, TF-IDF vectorizer for pre-processing and double layer Bi-LSTM for extracting the refined features to obtain better learning.
Journal ArticleDOI
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
TL;DR: Zhang et al. as discussed by the authors proposed a Similarity-Aware Multimodal Prompt Learning (SAMPLE) framework, which incorporates prompt learning into multimodal fake news detection.
Journal ArticleDOI
Meta-prompt based learning for low-resource false information detection
TL;DR: The authors proposed a meta-prompt based learning framework for low-resource false information detection, which combines meta-learning and prompt learning to solve the problem of the randomly initialized template hindering excavation performance and learn optimal initialized parameters by borrowing the benefit of meta learning in fast parameter training.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.