Exploring the Power of Dual Deep Learning for Fake News Detection
Abstract
The rise of social media has intensified the spread of fake news, a problem further exacerbated by generative artificial intelligence (AI). Hence, the need for improved detection of both human-created and AI-generated fake news using advanced AI models is critical. This paper proposes a survey to assess knowledge and attitudes towards news and AI, combining demographic data, personality traits, and the ability to distinguish between real and AI-generated news. Additionally, we create a new dataset, ERAF-News, containing real, fake, AI-generated true, and AI-generated fake news. To classify different types of news, we developed a dual-stream transformer model, DuSTraMo. This model leverages the capabilities of two parallel transformers to enhance the accuracy of news classification. The survey, involving 83 participants from 9 countries, revealed that respondents struggle to differentiate human-generated from AI-generated news. Notably, BERT outperformed GPT-2 and BART in generating realistic text, and RoBERTa and DistilBERT achieved over 98% accuracy in fake news classification. Dual-GPT models also showed high accuracy.
This study underscores the effectiveness of the DuSTraMo model and the ERAF-News dataset in enhancing the detection of both human-created and AI-generated fake news. The findings highlight the increasing dominance of AI in this domain and the pressing need for advanced methods to combat fake news. Additionally, a survey examining users’ responses to fake news reveals a concerning inability to accurately
identify false information.
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DOI: https://doi.org/10.31449/inf.v48i4.5977
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