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entry · p2026-09-06-037 · validated

Learned email spam filtering (Gmail)

1.5 billion people use Gmail every month, and 5 million paying businesses use Gmail in the workplace
tech
learned-spam-filter
passes the definition as
Bayesian and later neural classifiers fit to labelled spam/ham and to user 'report spam' feedback
from
2004
region
world
domain
access
side
benefit
people
1,500,000,000 users (2019-02)
effect
100,000,000 messages per day (2019-02) — additional spam messages blocked per day by the TensorFlow models, on top of a >99.9% overall block rate
reversible
yes
who holds the switch
owner:Google
attribution
named
source kind
primary
the statistic
As of February 2019 Google reported 1.5 billion monthly Gmail users, more than 99.9 percent of spam/phishing/malware blocked, and around 100 million additional spam messages blocked per day by machine-learned models.
source
https://workspace.google.com/blog/product-announcements/ridding-gmail-of-100-million-more-spam-messages-with-tensorflow
proposed by
researcher (opus), brief 2026-09-06, era-1990-2011
received
2026-09-06T09:39:39-04:00
harvest report
impacts/harvest/2026-09-06-era-1990-2011.md

Rulings, in order

  1. 2026-09-06T09:39:47-04:00 · validatorpass — quote found on the fetched page (exact match; HTTP 200 text/html; charset=utf-8)
  2. 2026-09-06T09:59:36-04:00 · sessionseverity — spam kept out: a convenience
The proposal as it arrived (JSON)
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 "id": "p2026-09-06-037",
 "received": "2026-09-06T09:39:39-04:00",
 "proposer": "researcher (opus), brief 2026-09-06, era-1990-2011",
 "kind": "entry",
 "sha": "532257a20af1d83d",
 "data": {
  "kind": "entry",
  "tech": "learned-spam-filter",
  "tech_name": "Learned email spam filtering (Gmail)",
  "definition": "Bayesian and later neural classifiers fit to labelled spam/ham and to user 'report spam' feedback",
  "from": "2004",
  "region": "world",
  "domain": "access",
  "side": "benefit",
  "people": {
   "n": 1500000000,
   "unit": "users",
   "date": "2019-02"
  },
  "effect": {
   "n": 100000000,
   "unit": "messages per day",
   "date": "2019-02",
   "what": "additional spam messages blocked per day by the TensorFlow models, on top of a >99.9% overall block rate"
  },
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  "switch": "owner:Google",
  "statistic": "As of February 2019 Google reported 1.5 billion monthly Gmail users, more than 99.9 percent of spam/phishing/malware blocked, and around 100 million additional spam messages blocked per day by machine-learned models.",
  "source": "https://workspace.google.com/blog/product-announcements/ridding-gmail-of-100-million-more-spam-messages-with-tensorflow",
  "quote": "1.5 billion people use Gmail every month, and 5 million paying businesses use Gmail in the workplace",
  "attribution": "named",
  "source_kind": "primary"
 },
 "from_file": "impacts/harvest/2026-09-06-era-1990-2011.md"
}