{"title":"使用基于提示的方法进行跨域假新闻检测","authors":"Jawaher Alghamdi, Yuqing Lin, Suhuai Luo","doi":"10.3390/fi16080286","DOIUrl":null,"url":null,"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.","PeriodicalId":37982,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-Domain Fake News Detection Using a Prompt-Based Approach\",\"authors\":\"Jawaher Alghamdi, Yuqing Lin, Suhuai Luo\",\"doi\":\"10.3390/fi16080286\",\"DOIUrl\":null,\"url\":null,\"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. 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Cross-Domain Fake News Detection Using a Prompt-Based Approach
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.
Future InternetComputer Science-Computer Networks and Communications
CiteScore
7.10
自引率
5.90%
发文量
303
审稿时长
11 weeks
期刊介绍:
Future Internet is a scholarly open access journal which provides an advanced forum for science and research concerned with evolution of Internet technologies and related smart systems for “Net-Living” development. The general reference subject is therefore the evolution towards the future internet ecosystem, which is feeding a continuous, intensive, artificial transformation of the lived environment, for a widespread and significant improvement of well-being in all spheres of human life (private, public, professional). Included topics are: • advanced communications network infrastructures • evolution of internet basic services • internet of things • netted peripheral sensors • industrial internet • centralized and distributed data centers • embedded computing • cloud computing • software defined network functions and network virtualization • cloud-let and fog-computing • big data, open data and analytical tools • cyber-physical systems • network and distributed operating systems • web services • semantic structures and related software tools • artificial and augmented intelligence • augmented reality • system interoperability and flexible service composition • smart mission-critical system architectures • smart terminals and applications • pro-sumer tools for application design and development • cyber security compliance • privacy compliance • reliability compliance • dependability compliance • accountability compliance • trust compliance • technical quality of basic services.