# Cross-Domain Fake News Detection Using a Prompt-Based Approach
###### Abstract  
The proliferation of fake news poses a significant challenge in today's information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine learning (ML) approaches often struggle with the nuanced contextual understanding required for accurate news classification. To address these challenges, we propose a novel contextualized cross-domain prompt-based zero-shot approach utilizing a pre-trained Generative Pre-trained Transformer (GPT) model for fake news detection (FND). In contrast to conventional fine-tuning methods reliant on extensive labeled datasets, our approach places particular emphasis on refining prompt integration and classification logic within the model's framework. This refinement enhances the model's ability to accurately classify fake news across diverse domains. Additionally, the adaptability of our approach allows for customization across diverse tasks by modifying prompt placeholders. Our research significantly advances zero-shot learning by demonstrating the efficacy of prompt-based methodologies in text classification, particularly in scenarios with limited training data. Through extensive experimentation, we illustrate that our method effectively captures domain-specific features and generalizes well to other domains, surpassing existing models in terms of performance. These findings contribute significantly to the ongoing efforts to combat fake news dissemination, particularly in environments with severely limited training data, such as online platforms. fake news detection; cross-domain evaluation; text classification; transfer learning +
Footnote †: journal: _Future Internet_  
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tasks. This success is attributed to their ability to capture contextualized information and encode rich knowledge about text through fine-tuning approaches. However, a major challenge hindering the full potential of PLMs is their reliance on vast amounts of labeled training data. The scarcity of annotated data presents a significant bottleneck in the NLP community, necessitating innovative approaches to leverage the power of PLMs even with limited training data. Recent advancements in few-shot learning techniques offer promising solutions to this challenge. Models such as GPT-3 [2] have demonstrated remarkable performance on few-shot tasks, highlighting the potential for effective application with limited data. Nevertheless, these models often come at the cost of high computational requirements due to their massive parameter size (e.g., GPT-3 with its 175B parameters), making them less accessible for widespread deployment. Inspired by the remarkable ability of PLMs to capture deep contextual information, researchers are actively developing complementary strategies to enhance their capabilities. These strategies aim to reduce the complexity of PLMs while simultaneously augmenting their ability to understand context. This combined approach empowers PLMs to achieve outstanding results even in scenarios with limited or no labeled data, thus expanding their applicability across diverse NLP tasks. For instance, recent advancements in NLP have illuminated the transformative potential of prompt-based learning strategies in harnessing the capabilities of PLMs across diverse applications. Prompts are usually used as prefixes that serve as additional information or context that guides the model's generation process. Extensive research, as demonstrated in [2; 3; 4; 5; 6; 7], has underscored the profound impact of prompts on enhancing PLM performance. The authors of [2] demonstrated exceptional performance in few-shot learning scenarios, relying solely on a natural language prompt and a limited number of task demonstrations as the input context. [PERSON] et al.