In this episode, Lucas and Luna dive into the growing role of natural language processing in detecting online misinformation. They explore a specific case study: how DataGPT, a startup, built a fact-checking bot that flags false claims in real-time on social media. Lucas breaks down the technical stack — transformer models like BERT fine-tuned on fact-checking datasets, with a focus on stance detection and claim verification. Luna questions the reliability of automated fact-checking and raises the issue of adversarial attacks on NLP models. They discuss the 2024 US election as a major test case, where the bot achieved 83% accuracy. The episode also touches on the ethical trade-offs: is automated fact-checking effective or does it risk censorship?

#NaturalLanguageProcessing #Misinformation #FactChecking #DataGPT #BERT #TransformerModels #StanceDetection #ClaimVerification #2024Election #AIEthics #TechEthics #AdversarialAttacks #SocialMedia #Technology #FexingoBusiness #BusinessPodcast #DataScience #MachineLearning

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