# How Data Scientists Use NLP to Detect Misinformation

Page: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-nlp-to-detect-misinformation
Text version: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-nlp-to-detect-misinformation.md
Podcast: [The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations](https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831)
Published: 2026-06-12T20:20:25+00:00
Episode link: https://audio.fexingo.com/business/the-data-science-podcast/episode-0047.mp3
Audio file: https://audio.fexingo.com/business/the-data-science-podcast/episode-0047.mp3
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Duration seconds: 584

## Resource

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 Keep every episode free: buymeacoffee.com/fexingo

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## Transcript

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