Tag
An opinion piece arguing that AI systems, especially large language models, are fundamentally bullshitters because they generate plausible but false information without understanding or intent to deceive.
NewsGuard launches an AI chatbot that aggregates news only from reliably rated sources and shares 50% of subscription revenue with publishers, aiming to combat misinformation and support journalism.
This paper introduces ComRate, a large-scale dataset of community notes and ratings from X, and proposes MultiCom, a persona-guided multi-agent framework for simulating community note evaluation. The approach achieves 84.7% accuracy in predicting note helpfulness.
Meta launches AI Mode in Facebook search, which grounds its answers in public posts from Facebook and Instagram, raising concerns about misinformation. Early testing shows mixed results with trip planning and some controversial responses.
AI-generated deepfakes are becoming more realistic and harder to detect, raising concerns about their use in spreading misinformation during midterm elections.
Right-wing officials and data center investors claim Chinese government is funding opposition to data centers, but experts are skeptical, saying domestic US actors lead the anti-data-center conversation.
Tech millionaires claim China is funding local opposition to U.S. data centers, but evidence is lacking; OpenAI found a limited Chinese influence campaign using ChatGPT to generate anti-data-center content.
This paper from Meta and Carnegie Mellon presents a multi-modal vision-language model pipeline for detecting AI-generated content on social media, achieving state-of-the-art performance and positive downstream impacts on user engagement.
The American College of Obstetricians & Gynecologists (ACOG) released its own maternal immunization schedule diverging from CDC recommendations under Health Secretary RFK Jr., recommending COVID-19 and flu vaccines that the CDC dropped amid anti-vaccine policy changes.
ChatGPT has been caught recommending fake scam websites and cloned stores of defunct brands, raising concerns about its training data being poisoned and the safety of AI-powered shopping assistants.
The article analyzes how AI-generated fake experts, protest posters, and synthetic photos of Trump are eroding the credibility of news media, pointing out that the media's lack of verification of information sources leads to the spread of misinformation.
A new MIT Media Lab study found that people who rely on AI chatbots to verify news become worse at detecting misinformation on their own, highlighting the 'AI dependency paradox.'
Introduces KITE, a tri-modal transformer framework that jointly models text, images, and knowledge graphs for fake news detection, outperforming unimodal and bimodal baselines on benchmark datasets.
Polymarket and Kalshi have asked influencer partners to remove paid partnership tags from posts that question election results, as the platforms enforce policies against election denial narratives.
This paper presents an adversarial methodology for creating and detecting AI-generated social bot content, curating a multilingual, cross-platform dataset of paired human and AI messages. Training on this adversarial data yields detection that significantly outperforms existing content-based bot detection models in real-world settings.
Elon Musk announces a new X feature: users who interact with a misleading post later corrected by Community Notes will receive an 𝕏 Chat message with the correction to address misperceptions.
The author reflects on how the absence or reduced role of Meta's content moderation has negatively impacted the information landscape.
Researchers from Kennesaw State University investigate cross-prompt generalization in detecting AI-generated fake news using interpretable linguistic features (lexical diversity, readability, emotion). A random forest classifier trained on one prompting strategy and tested on another achieves AUC values of 0.988–1.000, suggesting these features capture stable, generalizable properties of AI-generated text.
The article examines how AI-generated disinformation (slop) is infiltrating grassroots activism against AI data centers, with fake memes and inaccurate search summaries fueling distrust and confusion.
This paper introduces BOUTEF, a large-scale multilingual corpus for studying fake news in Algeria and Tunisia, covering Arabic dialects, Arabizi, French, English, and code-switching. It includes empirical analysis of linguistic strategies and engagement dynamics.