How the monitored publications were assessed

Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.

14 items read as mixed or uncertain

• Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
  -16 Google DeepMind 2026-10-14 low agreement · ±150
• AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases
  -10 arXiv cs.CY (Computers and Society) 13d ago low agreement · ±125
• Guarding the Gates of Knowledge
  -7 IEEE Spectrum: AI 7d ago low agreement · ±110
• Beyond the Monolithic Interface: A Research Agenda for Plural Conversational AI
  -1 arXiv cs.CY (Computers and Society) 2026-10-12 low agreement · ±120
• Ghost in the machine: how AI is haunting the search for the supernatural
  0 The Guardian: Artificial Intelligence 9d ago single model
• To Manipulate or Not: Opinion Dynamics in LLM Interactions
  0 arXiv cs.AI 2026-10-23 high agreement · ±20
• Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting
  0 arXiv cs.CY (Computers and Society) 2026-10-15 single model
• Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged
  0 arXiv cs.CY (Computers and Society) 2026-09-21 single model
• When Does the Public Become Suspicious of Bots? Demand-Side Evidence from Botometer Query Logs
  0 arXiv cs.CY (Computers and Society) 2026-09-18 single model
• Wednesday briefing: Why tech companies might be only too happy for us to believe AI will ‘kill us all’
  0 The Guardian: Artificial Intelligence 2026-09-16 single model
• AI Models Are Watermarking Text—Will You Notice?
  0 IEEE Spectrum: AI 2026-09-09 single model
• Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models
  +2 arXiv cs.CY (Computers and Society) 2026-09-16 moderate agreement · ±40
• Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation
  +10 arXiv cs.CY (Computers and Society) 2026-09-28 single model
• Demystifying the Privacy-Utility Trade-off in LLM Interactions
  +14 arXiv cs.AI 2026-09-12 low agreement · ±170

Across the whole window

The two tables below describe every item in the last 365 days under misinformation. They are deliberately not narrowed by the reading filter (currently mixed or uncertain), because a mean taken only over items already selected for their score would just restate the filter.

By topic

By source

A source's mean says how the items it published were read. It is not a rating of the source.