Is Climate Change a Hoax: Fake News Detection for Sustainability News

Date

2024-03-20

Author

Özdemir, Serkan

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Fake news and misleading information have long been recognized as significant concerns throughout human history, perpetually challenging the integrity of information dissemination. With the pervasive rise of social media platforms, the velocity at which fake news spreads has escalated exponentially, intensifying the challenge of separating fact from fiction. Concurrently, the dimensions of fake news datasets have expanded in tandem with social media enhancements, facilitating the development of more sophisticated detection mechanisms. Of particular concern is the proliferation of deceptive climate-related news disseminated through various media channels, aimed at manipulating public perceptions and influencing behavioral patterns. This deceptive narrative underscores the urgency of implementing robust strategies to combat misinformation, especially in domains as crucial as climate science and sustainability. In this paper, a comprehensive analysis employing Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Bidirectional Long-Short Term Memory (BiLSTM) algorithms is conducted to classify fake news effectively. The efficacy of these methodologies is assessed through a battery of evaluation metrics, including accuracy, F1-score, and Matthews Correlation Coefficient (MCC), providing insights into their respective strengths and limitations. The findings underscore the superior performance of the LSTM algorithm in detecting fake news, particularly within the context of sustainability-related articles. This discernment highlights the critical role of advanced machine learning techniques in fortifying information integrity and combating the pervasive dissemination of misinformation in contemporary discourse.

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https://as-proceeding.com/index.php/icada
https://hdl.handle.net/11511/111627

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Graduate School of Informatics, Conference / Seminar