Methodology

How we measure the Dead — AI-generated content and bot activity across the web.

Overview

The Dead Internet Monitor tracks two distinct phenomena: the creation of AI-generated content (“AI Slop”) and the consumption of content by automated accounts (“AI Slurp”). We sample content from major platforms, classify it using large language models, and analyse author behaviour for bot-like patterns.

Our goal is not perfect accuracy — which remains elusive even for specialised detectors — but consistent, transparent measurement of trends over time.

+-----------------------+
|      COLLECTION       |  7 sources, staggered
+-----------+-----------+
            |
      +-----+-----+
      v           v
+----------+ +----------+
| CLASSIFY | |BOT DETECT|  parallel
|  (LLM)   | |(7-signal)|
+----+-----+ +----+-----+
     |            |
     +------+-----+
            v
+-----------------------+
|      AGGREGATION      |  DII + Autopsy Matrix
+-----------+-----------+
            v
+-----------------------+
|       DASHBOARD       |  deadinternetmonitor.com
+-----------------------+

Data Collection

Content is collected from seven sources on staggered schedules. The headline Dead Internet Index weights each source equally regardless of volume, so no single platform dominates the aggregate.

Reddit uses public RSS feeds (comments only, no API credentials required). Mastodon and Lobsters serve as control groups — decentralised or invite-only platforms with lower bot incentive.

Classification

Each item is classified by a large language model using a structured prompt (v3.0) that applies Bayesian calibration with platform-specific base rates derived from Ahrefs and Originality.ai research. This counters the documented tendency of LLM classifiers to default to “human” ( RAID 2024 found 10–15% false negative rates).

Models

Fallback triggers when primary confidence is below 0.5.

AI Indicators

The classifier looks for research-validated signals of AI generation:

• Overly balanced, hedging language (“It's worth noting”, “to be fair”)
• Formulaic structure and template phrases (“Let's dive in”, “In the realm of”)
• Comprehensive but shallow coverage of topics
• Synthetic empathy (“Great question!” without substance)
• Absence of personality, humour, or strong opinions
• Safety disclaimers on simple topics

Human Indicators

• Personal anecdotes and specific lived experiences
• Typos, slang, mid-thought corrections
• Strong opinions without both-sides framing
• Niche expertise with personal perspective
• Genuine emotional expression — frustration, sarcasm, humour
• Natural digressions and terse replies

Output

Each classification returns: a label (ai_generated, human_created, or uncertain), a confidence score (0.0–1.0), specific indicators observed, and a brief reasoning explanation.

Post-Processing

After the LLM returns its classification, a post-processing step corrects for the documented human-default bias. Items classified as “human” but carrying multiple AI indicators are reclassified as uncertain or AI-generated. Short content (<100 characters) is capped at 0.60 confidence.

+--------------+
| Content Item |
+------+-------+
       v
+--------------+   confidence
|Primary Model |---- >= 0.5 --> RESULT
| Gemini Flash |
+------+-------+
       | < 0.5
       v
+--------------+
|Fallback Model|---- >= 0.5 --> RESULT
| Claude Haiku |
+------+-------+
       | < 0.5
       v
  "uncertain"
       |
       v
+--------------+
|Post-Process  |  correct false negatives
| Recalibrate  |  using signal evidence
+------+-------+
       v
   FINAL LABEL
ai / human / uncertain

Bot Detection

Separate from content classification, we analyse author behaviour using a 7-signal weighted scoring system grounded in peer-reviewed research. Authors with 2+ collected items receive a bot score.

Scores above 0.7 are flagged as likely bots. Between 0.4–0.7 is suspicious. Below 0.4 is likely human.

The Autopsy

What type of Dead is the Internet?

The Autopsy Matrix crosses content origin with audience type. The content axis comes from our AI classification. The audience axis uses the global bot consumption rate — the proportion of web traffic that is automated.

AI Slop = AI-generated content · AI Slurp = bot consumption of content

• Alive — Human-written content reaching a human audience. The real internet.
• Zombified — Human-written content consumed by bots. Real voices talking to no one.
• Polluted — AI-generated content reaching human eyes. Slop you actually read.
• Dead — AI-generated content consumed by bots. Machines talking to machines.

The bot consumption rate (45%) is a cross-industry blend of four major bot traffic reports: Imperva 2025 (51%), Akamai 2025 (51%), Cloudflare Radar 2025 (~30%), and Barracuda 2023 (50%). This rate is applied uniformly across all sources as per-platform consumption data is not publicly available.

Dead Internet Index

The DII is a composite score (0–100) measuring how “dead” the internet is. Each source's DII is computed independently, then the headline DII is the unweighted mean across all sources — giving every platform equal say regardless of collection volume. Each per-source DII combines four weighted components:

When consumption data is available (Cloudflare Radar, robots.txt monitoring), a fifth component (0.20 weight) is added and the other weights adjust downward.

Limitations

• Classification is imperfect. Skilled writers can produce polished text that resembles AI output, and AI can mimic human writing. We target consistency over perfection.
• Sampling bias. We monitor a limited set of platforms. Findings may not generalise to the entire internet. We will continue to increase the number of sources and samples.
• Short content is harder. Posts under 100 characters are capped at 0.60 confidence.
• Temporal drift. As AI-generated content evolves, detection patterns must be updated. We version our prompts and models to track changes.
• Engagement data varies. Some sources (e.g. Reddit via RSS) do not provide vote scores, so bot engagement metrics are unavailable for those platforms. The bot engagement average only includes sources where engagement data exists.
• Text only. Currently, the monitor only measures text-based content. Future iterations will be multimodal.

Transparency

Every classification record stores the model provider, model name, prompt version, token counts, estimated cost, and latency. This metadata enables full auditability and comparison across models over time.

Revision History

The monitor evolves as we add sources, refine methodology, and respond to platform changes. Each revision is backfilled — we re-aggregate all historical data under the current methodology so the full timeline is consistent.

The trend matters more than any single number. We are watching the watchers.