Growth/Personalized first day/Newcomer tasks/Experiment analysis, November 2020/ja

Growth チーム は2019年11月、「新規登録者向けタスク」機能を新規登録者のホームページに追加しました. この機能は編集の対象としてタスクをお勧めするフィードを発信し、範囲は新人の興味に合わせてあります. その趣旨とは、それぞれのウィキでは到着着後の新人が関心を持てるよう、簡単な編集作業を提供することを目指します. 事前の仮説では、新人にとってこのツールは編集を試してみたくなり、編集技能を習得し自分の編集の影響を理解して、編集をその後も続けるきっかけになると想定しました.

機能の影響度を計測するには、条件付きの実験を実施しています. 新規登録者の 76% に機能を提供、残り 24% は対象外としました. 実験期間は6ヵ月とし、対象としたウィキペディアの言語版はアラビア語、 ベトナム語、チェコ語、朝鮮語です.

わかったことの概要
In general, the analysis showed that the Growth features improve outcomes for newcomers. Below are the most important points.


 * Newcomers who get the Growth features are more likely to be "activated" (i.e. make a first article edit).
 * We believe they are also more likely to be retained (i.e. come back and make another article edit on a different day).
 * The features also increase edit volume (i.e. number of edits) without reducing constructiveness (i.e. if edits are reverted).

We believe that these results confirm that the Growth features, in particular newcomer tasks, lead newcomers to edit more and lead newcomers to stay on the wiki for longer.

Because of these results, we think all Wikipedias should consider implementing these features.

We also believe that these results validate that the Growth team should continue to work on structured tasks, to create new kinds of easy editing workflows for newcomers.

用語集

 * As of November 2020, seventeen wikis have the Growth features. However, in our experiment, we analyzed four pilot wikis: Arabic, Vietnamese, Czech, and Korean Wikipedias.
 * Not all newcomers receive Growth features; 20% of them are randomly chosen to get the default experience. The group with the features is the treatment group and the group with the default experience is the control group. Numbers that come from the default experience are called baseline numbers.
 * Activation is defined as a newcomer making their first edit within 24 hours of registration. The baseline activation rate is the activation rate with the default features, not the Growth features.
 * Constructive activation is defined as a newcomer making their first edit within 24 hours of registration, and that edit not being reverted within 48 hours. The baseline constructive rate is the rate for users with the default features, not the Growth features.
 * Retention is defined as a newcomer coming back on a different day in the following two weeks after activation and making another edit. The baseline retention rate is the rate for users with the default features, not the Growth features.
 * Edit volume is the overall count of edits made in a user's first two weeks. The baseline edit volume is the count for users with the default features, not the Growth features.

Detailed findings
Below are the specific impacts we estimated from the controlled experiment. These are all based on observing 97,755 new accounts on the pilot wikis, between November 2019 and May 2020. For more specifics on the methodology, see "Methodology" below.



Activation
For this analysis, we focused on edits to the Article and Article Talk namespaces.


 * Activation: newcomers with Growth features are 11.6% more likely to make a first article edit. On our four pilot wikis, the baseline activation rate is 21.6%.  The Growth features are estimated to increase activation to 24.1%, which is an 11.6% increase over the baseline.
 * Constructive activation: the effect is larger when looking only at constructive activation. Newcomers with Growth features are 26.7% more likely to make a first unreverted article edit.  On our four pilot wikis, the baseline constructive activation rate is 16.1%.  The Growth features are estimated to increase this to 20.4%, which is a 26.7% increase over the baseline.



Retention
Because retention is much more rare than activation, it is harder to detect changes. In this experiment, we did not detect any changes directly. Instead, we estimate that retention is increased to a similar degree that activation is increased, i.e. by about 11.6%. This comes from the idea that activity during the first day affects activity on the following days, something we account for in our statistical models. Since the Growth features are found to increase the number of users who are active on their first day, and we find no change in the probability that activated users are retained, it follows that we can expect the increase in activation to translate into a similar increase in retention. In other words, the Growth features appear to lead to an increase in retention caused by the increase in activation: some of the users that the Growth features activated would naturally go on to being retained.

The baseline retention rate across the four wikis in the experiment is 3.2%. We estimate that the Growth features increase this to 3.6%.



編集の量
Growth 機能は初学者が登録後の2週間で手がける記事編集を 22% 増に導きました. パイロットケースのウィキペディアの4言語版では、編集量の基本線は1.4回、すなわち平均的な初学者は平均1.4回の編集を実行します. 初学者で Growth 機能を使った人の編集回数は平均で1.7回です.

言い換えると次のようになります.


 * Growth 機能のない 初学者1,000人は編集を1,400回実施.
 * Growth 機能のある 初学者1,000人は編集を1,700回実施.

この増分は Growth 機能により初学者が記事を編集する可能性を増やし、さらに 初学者で短期間に多くのおすすめ編集をこなす人たちがいます. 中には登録後2週間で100回超の編集をした人もいました.

その他の指標
We also looked at several other metrics, with less significant findings.


 * Reverts: we looked at whether newcomers with Growth features were more or less likely to have their edits reverted. This analysis did not show large or clear results.
 * Highly active newcomers: our results have shown that Growth features cause more newcomers to become active and to make more edits. We also wanted to see whether the features lead to more newcomers becoming highly active. We defined them as users making 50 edits in their first 30 days. This analysis did not show differences resulting from the Growth features.
 * Thanks: we looked at whether newcomers with Growth features receive more “thanks” than other newcomers. We found similar results to the retention analysis in that we expect that Growth features do lead to more thanks received, but that this is only because they cause more edits. This is not because the features cause newcomers to make edits that are more likely to attract thanks.
 * Differences between wikis and platforms: we compared the wikis and platforms (mobile vs desktop). We did not find significant differences in the effect of the Growth features.

Takeaways

 * The features work: the Growth team features work to increase newcomer engagement. This is especially true for the "newcomer tasks" component, which suggests easy edits.
 * Confidence in building structured tasks: this gives us confidence that our current work to build more kinds of newcomer tasks, such as the "add a link" task, will increase impact.
 * Need for positive reinforcement: the results showed that the Growth features primarily impact activation – getting newcomers to make their first edit – as opposed to retention. The features only seemed to increase retention because they increased activation.  The Growth team should think about what we can add to the features to encourage newcomers to return after making their first edits.  Thus, we are planning work on "positive reinforcement" this year. This will add milestones and statistics, so that newcomers can get excited about their progress and impact.

次のステップへ

 * もっと広く知ってもらう：機能の価値に自信を深めることができました. そこで Growth チームは、さらに多くのウィキに実験結果を読んでもらい、機能の導入を働きかけていきます.
 * タスクの継続：今年は新しい種類のタスクを増やすことに注力し、編集初学者が編集を完了した時点により積極的な強化を盛り込む予定です.
 * 分析結果の援用：この分析を終え、将来、同じ実験をするときにより円滑に実施する準備ができました. 導入するウィキが増えると、この機能の影響力の分析をして、初期の影響に続く向上を把握できます.

方法論
Growth チームは2019年11月21日、新規登録者タスクのモジュールをウィキペディアの言語版とからチェコ語、朝鮮語、ベトナム語、アラビア語のホームページに導入しました. 実験期間中に利用者を不作為に調査群と対照群に振り分けました. 調査群の使用者にはGrowth 機能をすべて支給し (ホームページ、新規登録者向けタスク、ヘルプパネルなど)、対照群の利用者には何も渡しません.

実験期間は2019年11月21日から同12月12日とし、調査群に入る確率は 50% でした. 次にチームが12月12日に新規登録者向けタスクにふたつの変数を取り入れた A/B テストを開始すると、80% に増加しました.

利用者は時期を選び Growth 機能を個人設定で有効にも無効にもできました. 無効にした利用者はこの調査から除外しました. また除外対象にはテストアカウント、 API 経由で登録した人 (ほとんどはアプリ自体のアカウント) と、自動登録されたアカウントがありました.

今回の分析には登録アカウント 9万7755 件をデータセットとして使用、期間は実験開始日から2020年5月14日までです. そのうちの 2万3529 件 (24.1%) は対照グループとし、実験対象は 7万4226 件 (75.9%) でした.

分析にはmultilevel (hierarchical) regression モデルを多用し、それぞれのウィキを集合変数に設定しました. これにより分析に現れるウィキ間の差異を表現できました. 一例として activation モデルに multilevel logistic regression モデルを適用、ウィキ間に見るactivation 率の差異を構成します. また編集活動は long tail distribution のパターンを辿るとわかっていることから、編集回数のモデル値には zero-inflated negative binomial distribution モデルを採用しています（ここでも multilevel モデルを適用）.