Moderator Tools/Automoderator/テスト
コミュニティがAutomoderator の精度をテスト・評価できるように、過去の編集とAutomoderatorがそれをリバートしたかどうかのデータを含むテスト用スプレッドシートを用意しています。
オートモデレーターの決定は、機械学習モデルによる採点と内部設定の組み合わせによって決まります。 モデルは再トレーニングにより時間とともに改善されますが、追加で内部ルールを定義することで、その精度を高めることを検討しています。 たとえば、Automoderatorは、利用者が自分の編集内容をリバートする行為を荒らしと誤認することしばしば観測されます。 こういった問題を改善するために、このボットの癖を探しています。同様の例の特定にご協力をお願いいたします。
このテストは必ずしもAutomoderatorの最終形式を反映しているわけではないことに注意してください。あくまでこのテストの結果を使用してテストを改善します。
Automoderatorのテスト方法
- If you have a Google account:
- Use the Google Sheet link below and make a copy of it
- You can do this by clicking File > Make a Copy ... after opening the link.
- After your copy has loaded, click Share in the top corner, then give any access to swaltonwikimedia.org (leaving 'Notify' checked), so that we can aggregate your responses to collect data on Automoderator's accuracy.
- Alternatively, you can change 'General access' to 'Anyone with the link' and share a link with us directly or on-wiki.
- Use the Google Sheet link below and make a copy of it
- Alternatively, use the .ods file link to download the file to your computer.
- After adding your decisions, please send the sheet back to us at swaltonwikimedia.org, so that we can aggregate your responses to collect data on Automoderator's accuracy.
After accessing the spreadsheet...
- Follow the instructions in the sheet to select a random dataset, review 30 edits, and then uncover what decisions Automoderator would make for each edit.
- Feel free to explore the full data in the 'Edit data & scores' tab.
- If you want to review another dataset please make a new copy of the sheet to avoid conflicting data.
- Join the discussion on the talk page.
Alternatively, you can simply dive in to the individual project tabs and start investigating the data directly.
We welcome translations of this sheet - if you would like to submit a translation please make a copy, translate the strings on the 'String translations' tab, and send it back to us at swaltonwikimedia.org.
If you want us to add data from another Wikipedia please let us know and we would be happy to do so.
Automoderatorについて
Automoderator’s model is trained exclusively on Wikipedia’s main namespace pages, limiting its dataset to edits made to Wikipedia articles. Further details can be found below:
内部コンフィグレーション
In the current version of the spreadsheet, in addition to considering the model score, Automoderator does not take actions on:
- Edits made by administrators
- Edits made by bots
- Edits which are self-reverts
- New page creations
The datasets contain edits which meet these criteria, but Automoderator should never say it will revert them. This behaviour and the list above will be updated as testing progresses if we add new exclusions or configurations.
注意度
In this test Automoderator has five 'caution' levels, defining the revert likelihood threshold above which Automoderator will revert an edit.
- At high caution, Automoderator will need to be very confident to revert an edit. This means it will revert fewer edits overall, but do so with a higher accuracy.
- At low caution, Automoderator will be less strict about its confidence level. It will revert more edits, but be less accurate.
The caution levels in this test have been set by the Moderator Tools team based on our observations of the models accuracy and coverage. To illustrate the number of reverts expected at different caution levels see below:
日次編集 | 日次編集のリバート | Automoderatorによる1日の平均リバート | |||||
---|---|---|---|---|---|---|---|
とても注意深い
>0.99 |
そこそこ注意深い
>0.985 |
少しは注意している
>0.98 |
不注意
>0.975 |
慎重でない
>0.97 | |||
英語版ウィキペディア | 140,000 | 14,600 | 152 | 350 | 680 | 1,077 | 1,509 |
フランス語版ウィキペディア | 23,200 | 1,400 | 24 | 40 | 66 | 98 | 136 |
ドイツ語版ウィキペディア | 23,000 | 1,670 | 14 | 25 | 43 | 65 | 89 |
スペイン語版ウィキペディア | 18,500 | 3,100 | 57 | 118 | 215 | 327 | 445 |
ロシア語版ウィキペディア | 16,500 | 2,000 | 34 | 57 | 88 | 128 | 175 |
日本語版ウィキペディア | 14,500 | 1,000 | 27 | 37 | 48 | 61 | 79 |
中国語版ウィキペディア | 13,600 | 890 | 9 | 16 | 25 | 37 | 53 |
イタリア語版ウィキペディア | 13,400 | 1,600 | 40 | 61 | 99 | 151 | 211 |
ポーランド語版ウィキペディア | 5,900 | 530 | 10 | 16 | 25 | 35 | 45 |
ポルトガル語版ウィキペディア | 5,700 | 440 | 2 | 7 | 14 | 21 | 30 |
ヘブライ語版ウィキペディア | 5,400 | 710 | 16 | 22 | 30 | 38 | 48 |
ペルシア語版ウィキペディア | 5,200 | 900 | 13 | 26 | 44 | 67 | 92 |
韓国語版ウィキペディア | 4,300 | 430 | 12 | 17 | 23 | 30 | 39 |
インドネシア語版ウィキペディア | 3,900 | 340 | 7 | 11 | 18 | 29 | 42 |
トルコ語版ウィキペディア | 3,800 | 510 | 4 | 7 | 12 | 17 | 24 |
アラビア語版ウィキペディア | 3,600 | 670 | 8 | 12 | 18 | 24 | 31 |
チェコ語版ウィキペディア | 2,800 | 250 | 5 | 8 | 11 | 15 | 20 |
ルーマニア語版ウィキペディア | 1,300 | 110 | 2 | 2 | 4 | 6 | 9 |
クロアチア語版ウィキペディア | 500 | 50 | 1 | 2 | 2 | 3 | 4 |
... | ... | ... | ... | ... | ... | ... | ... |
All Wikipedia projects | 538 | 984 | 1,683 | 2,533 | 3,483 |
ウィキメディアの他のプロジェクトに関しても、このデータをこちらでご参照ください。
特定の編集を採点する
簡単なユーザスクリプトを作成しましたので、個別の編集に対して巻き戻しリスク・スコア(Revert Risk score)を取得する場合に使ってください。
単に User:JSherman (WMF)/revertrisk.js をご自分が使用する commons.jsにインポートし、mw.loader.load( 'https://en.wikipedia.org/wiki/User:JSherman_(WMF)/revertrisk.js?action=raw&ctype=text/javascript' );
を指定します。
するとサイドバーのツールメニューに 'Get revert risk score' が表示されるはずです。 ここにはモデルの評点しか表示されないこと、またオートモデレータの内部設定を前述ほど詳しく示していないことにご留意ください。 上の得点表は、オートモデレータが偽陽性を正しく判定している確率を検出しようとして、当チームが設定した最低点を列記してあります。
Initial results
Quantitative
22 testing spreadsheets were shared back with us, totalling more than 600 reviewed edits from 6 Wikimedia projects. We have aggregated the data to analyse how accurate Automoderator would be at different caution levels:
Not cautious (0.97) | Low caution (0.975) | Somewhat cautious (0.98) | Cautious (0.985) | Very cautious (0.99) |
---|---|---|---|---|
75% | 82% | 93% | 95% | 100% |
In our Moderator Tools/Automoderator/Measurement plan we said that we wanted the most permissive option Automoderator could be set at to have an accuracy of 90%. The ‘Not cautious’ and ‘Low caution’ levels are clearly below this, which isn’t surprising as we didn’t have clear data from which to select these initial thresholds. We will be removing the ‘Not cautious’ threshold, as a 25% error rate is clearly too low for any communities. We will retain ‘Low caution’ for now, and monitor how its accuracy changes as model and Automoderator improvements occur leading up to deployment. We want to err on the side of Automoderator not removing bad edits, so this is a priority for us to continue reviewing.
When we have real world accuracy data from Automoderator's pilot deployment we can investigate this further and consider changing the available thresholds further.
Qualitative
On the testing talk page and elsewhere we also received qualitative thoughts from patrollers.
Overall feedback about Automoderator’s accuracy was positive, with editors feeling comfortable at various thresholds, including some on the lower end of the scale.
Some editors raised concerns about the volume of edits Automoderator would actually revert being relatively low. This is something that we’ll continue to discuss with communities. From our analysis (T341857#9054727) we found that Automoderator would be operating at a somewhat similar capacity to existing anti-vandalism bots developed by volunteers, but we’ll continue to investigate ways to increase Automoderator’s coverage while minimising false positives.
Next steps
Based on the results above, we feel confident in the model’s accuracy and plan to continue our work on Automoderator. We will now start technical work on the software, while exploring designs for the user interface. We expect that the next update we share will contain configuration wireframes for feedback.
In the meantime please feel free to continue testing Automoderator via the process above - more data and insights will continue to have a positive impact on this project.