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The Silence of the LLMs: Cross-Lingual Analysis of Political Bias and False Information Prevalence in ChatGPT, Google Bard, and Bing Chat
10 references·3 citing·40 second hop
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References

  • 2023ChatGPT outperforms crowd workers for text-annotation tasks1,193 cited
  • 2023ChatGPT for Education and Research: Opportunities, Threats, and Strategies1,074 cited
  • 2019Algorithms of Oppression: How Search Engines Reinforce Racism197 cited
  • 2023The political ideology of conversational AI: Converging evidence on ChatGPT’s pro-environmental, left-libertarian orientation172 cited
  • 2023What ChatGPT Tells Us about Gender: A Cautionary Tale about Performativity and Gender Biases in AI161 cited
  • 2023ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning152 cited
  • 2024The Self-Perception and Political Biases of ChatGPT94 cited
  • 2020Personalizing the war: Perspectives for the adoption of news recommendation algorithms in the media coverage of the conflict in Eastern Ukraine56 cited
  • 2023Nationality Bias in Text Generation43 cited
  • 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation37 cited

Cited by

  • 2024How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments18 cited
  • 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Access16 cited
  • 2025AI chatbot accountability in the age of algorithmic gatekeeping: Comparing generative search engine political information retrieval across five languages

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16 cited