# Measures taken against bad behaviour in League of Legends

**URL:** <https://meta.discourse.org/t/measures-taken-against-bad-behaviour-in-league-of-legends/26084>\
**Category:** Community Building\
**Tags:** moderation\
**Created:** [2015 年3 月 6 日 21:49 UTC](https://meta.discourse.org/t/measures-taken-against-bad-behaviour-in-league-of-legends/26084 "2015-03-06T21:49:55Z")\
**Posts on this page:** 1\
**Showing post:** 4

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作者： ![codinghorror](https://sea3.discourse-cdn.com/meta/user_avatar/meta.discourse.org/codinghorror/32/110067_2.png) [@codinghorror](https://meta.discourse.org/u/codinghorror)\
发布日期： [2016 年4 月 16 日 06:47 UTC](https://meta.discourse.org/t/measures-taken-against-bad-behaviour-in-league-of-legends/26084/4 "2016-04-16T06:47:27Z")

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An update, with an emphasis on speed of feedback:

[http://www.nature.com/news/can-a-video-game-company-tame-toxic-behaviour-1.19647](http://www.nature.com/news/can-a-video-game-company-tame-toxic-behaviour-1.19647)

> “If you look at any classic literature on reinforcement learning, the timing of feedback is super critical,” says Lin. So he and his team used the copious data they were collecting to train a computer to do the work much more quickly. “We let loose machine learning,” Lin says. The automated system could provide nearly instantaneous feedback; and **when abuse reports arrived within 5–10 minutes of an offence, the reform rate climbed to 92%**. Since that system was switched on, Lin says, verbal toxicity among so-called ranked games, which are the most competitive — and most vitriolic — dropped by 40%. Globally, he says, the occurrence of hate speech, sexism, racism, death threats and other types of extreme abuse is down to 2% of all games.

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