News organizations introduce bias into their coverage via the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. Here, we introduce the Media Bias Detector, a scalable computational framework that integrates large language models (LLMs) with near-real-time news scraping to extract structured annotations, including political lean, tone, topics, article type, and major events, across hundreds of articles per day. We quantify these dimensions of coverage at the sentence level, the article level, and the publisher level, expanding the ways in which researchers can analyze selection and framing bias in the modern news landscape. We also release an interactive web platform for convenient exploration of these data and an accompanying dataset covering more than 140,000 articles published in 2024 by 10 prominent publishers. Last, we present some results derived from this dataset that illustrate how the MBD can uncover correlates of bias in news coverage.
Samar Haider, Amir Tohidi, Jenny S. Wang et al.· Science Advances· 0 citations
Personal intelligence is becoming a central frontier for user-facing AI agents. To be helpful in everyday life, agents must understand users across the digital contexts where their preferences, intents, habits, social relationships, and needs unfold over time. Today's systems can personalize within individual apps or tasks, but personal intelligence as a whole remains under-measured: how agents build cross-context user understanding, support steerable recommendation systems, act proactively across platforms, and avoid over-personalization. We introduce PersonaMem-v3, a real-world-grounded benchmark and evaluation harness for omni-platform personal intelligence. PersonaMem-v3 is seeded from more than one million anonymized real-world engagement histories, most of which are implicit signals, and uses them to construct time-indexed user digital worlds across social media, chatbot, calendar, and AI-companion with preference evolvement over time. The benchmark brings personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning into one framework, anchored in psychology, social-linguistics, and user-behavior theories. It evaluates whether AI agents can infer holistic user understanding from cross-platform evidence, personalize responses, rerank recommendations on social media, follow user steering through natural language, and hold back when personalization would be inappropriate, repetitive, outdated, or unnecessary. PersonaMem-v3 points toward LLM-powered personal intelligent agents that work with existing scalable recommendation infrastructure while making personalization more interactive, agentic, and aligned with how real users experience their digital lives.
Bowen Jiang, Yuan Yuan, Zhuoqun Hao et al.· 0 citations
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