Discourse monitor
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.
Mean discourse valence per day · misinformation · last 180 days
This line is the mean of every item scored on each day. It deliberately ignores the reading filter, because a daily mean of only the strongly adverse items would move with how many were filtered out rather than with the discourse.
Discourse valence, strongly adverse
-73
Strongly adverse
29 items · range -91 to -65
-100
-50
0
+50
+100
-73
Adverse readingFavourable reading
29 items read as strongly adverse
- “This is the AI men actually use”: Meta ads pushed apps nudifying real teens
- Microsoft exec called AI scraping the “largest theft of labor in human history”
- Meta continues to run ads promoting child sexual abuse material in India - report
- AI chatbots remove hijabs from images of Muslim women when prompted
- Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities
- As AI images become common, the threat to elections draws alarm: ‘How will voters know what’s true?’
- Auditing AI-Washing in German Startups: A Mixed-Methods Study of Marketing Claims and Perceptions
- When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects
- AI hallucination of Chinese nuclear components almost led to US military attack
- Man told ChatGPT he was feeling delusional. ChatGPT insisted he was Jesus.
- Understanding AI Provider Recommendations in Local Service Markets
- AI threatens democracy. But we are not powerless: here’s how to fight back | Carine Fouteau
- One Nation’s AI video is vile and tired – but the ensloppification of Australian politics doesn’t stop there
- The Triumph of Tech-Fueled Propaganda
- Russia’s Latest Propaganda? A.I.-Generated War Songs.
- I Used AI to Fake a Scientific Paper. Here’s What Happened
- LLM Recommenders Produce Demographic Inequality Through Content Preferences
- AI is stealing Indigenous Australian art and churning out ‘Temu slop’, artist says
- AI ‘deepfakes’ give US political attack ads a new twist
- RFK Jr. thinks AI will free us from the "tyranny" of medical facts, expertise
- Generative AI Produces and Transmits Beauty Bias
- Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
- What do AI chatbots do after users decline therapeutic redirection for suicide thoughts?
- AI-driven disinformation running rampant on social media before US midterms, experts warn
- Automated Disinformation and Malicious AI Swarms: Risks for Democracy and Development in Africa
- Lawsuit: Man introduced his wife to ChatGPT. She used it to plan his murder.
- Information Operations Exploit APIs to Manipulate Social Media
- Political Sorting Can Drive AI Models Apart Through User Feedback
- Tasmanian justice department review under way after AI and fake citation used in murderer’s parole decision
3560
discovered, all time
984
passed triage
2574
rejected at triage
3464
model assessments
710
multi-model panels
1780
distinct claims tracked
Across the whole window
The two tables below describe every item in the last 180 days under misinformation. They are deliberately not narrowed by the reading filter (currently strongly adverse), because a mean taken only over items already selected for their score would just restate the filter.
By topic
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 568 | -42 | |
| Governance and regulation | structural | 459 | -31 | |
| Concentration of power | risk | 451 | -36 | |
| Loss of control and alignment | risk | 337 | -49 | |
| Human agency and epistemics | risk | 166 | -26 | |
| Cyber capability | risk | 165 | -44 | |
| Evaluation and measurement | structural | 159 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 104 | -44 | |
| Employment and displacement | risk | 62 | -31 | |
| Medicine and health | benefit | 43 | -14 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 241 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 217 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 112 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 60 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |