FAKE DETECT: A DEEP LEARNING ENSEMBLE MODEL FOR FAKE NEWS DETECTION (ML)
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Abstract
The rise of ubiquitous deepfakes, misinformation, disinformation, and post-truth,
often referred to as fake news, raises concerns over the role of the Internet and social media inmodern democratic societies. Due to its rapid and widespread diffusion, digital deception has not only an individual or societal cost, but it can lead to significant economic losses or to risks to national security. Blockchain and other distributed ledger technologies (DLTs) guarantee the provenance and traceability of data by providing a transparent, immutable, and verifiable record of transactions creating a peer-to-peer secure platform for storing and exchanging information. This overview aims to explore the potential of DLTs to combat digital deception, describing the most relevant applications and identifying their main open challenges.Moreover, some recommendations are enumerated to guide future researchers on issues that will have to be tackled to strengthen the resilience against cyber-threats on today’s online.
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