Moderatör Araçları/Automoderator
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Automoderator
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Moderator Tools ekibi şu anda Wikimedia projeleri için bir 'automoderator' aracı oluşturmak için bir proje üzerinde çalışıyor. Bu araç moderatörlerin, bir makine öğrenimi modelinden alınan puanlamaya dayalı olarak hatalı düzenlemelerin otomatik olarak önlenmesini veya geri döndürülmesini yapılandırmasına olanak tanıyacak. Daha basit bir şekilde, bizler, ClueBot NG, SeroBOT, Dexbot, gibi vandalizm karşıtı botlar gibi benzer bir işlev gerçekleştiren bir yazılım oluşturuyoruz, ancak tüm dil toplulukları için kullanılabilir hale getiriyoruz. A MediaWiki extension is now under development - Extension:AutoModerator.
Bizim hipotezimiz şudur: Toplulukların bariz vandalizmi otomatik olarak önlemesini veya geri döndürmesini sağlarsak, moderatörlerin diğer faaliyetlere harcayacak daha fazla zamanı olacaktır.
2023 yılının geri kalanında bu fikri araştırıp keşfedeceğiz ve 2024 takvim yılı başlarında mühendislik çalışmalarına başlayabileceğimizi bekliyoruz.
Son güncelleme (Şubat 2024): Açılış ve konfigürasyon sayfalarının ilk sürümü için Tasarımlar yayınlandı. Tüm fikir ve önerilere açığız!
Son güncellemeler
- Şubat 2024: Test sürecimizden ilk sonuçları yayınladık.
- Ekim 2023: Bu projenin başarısını değerlendirmek için hangi verileri kullanmamız gerektiğine karar vermek için ölçüm planı hakkında girdi ve geri bildirim arıyoruz ve Automoderator'ın karar verme sürecine ilişkin girdi toplamak için test verilerini kullanıma sunduk.
- Ağustos 2023: Bu projenin ve diğer moderatör odaklı projelerin yakın zamanda Wikimania'da sunulduğunu gördük. Buradan konuşmaya ulaşabilirsiniz.
Motivasyon
Wikimedia projelerinde, bir sayfayı önceki durumuna geri döndürerek açıkça geri alınması gereken önemli sayıda düzenleme yapılmaktadır. Hizmetliler ve devriyeler bu düzenlemeleri manuel olarak gözden geçirmek ve geri almak için çok fazla zaman harcamak zorundadır, bu da birçok büyük vikide aktif moderatör sayısına kıyasla dikkat edilmesi gereken çok fazla iş olduğu hissine katkıda bulunur. Bu yükleri azaltarak moderatörlerin diğer görevler üzerinde çalışmak için zaman kazanmasını istiyoruz.
Reddit, Twitch ve Discord gibi birçok çevrimiçi topluluk web sitesi, topluluk moderatörlerinin belirli ve algoritmik otomatik moderasyon eylemlerinin bir karışımını kurabilmesi için 'otomatik moderasyon' işlevselliği sunar. On Wikipedia, AbuseFilter provides specific, rules-based, functionality, but can be frustrating when moderators have to, for example, painstakingly define a regular expression for every spelling variation of a swear word. It is also complicated and easy to break, causing many communities to avoid using it. At least a dozen communities have anti-vandalism bots, but these are community maintained, requiring local technical expertise and usually having opaque configurations. These bots are also largely based on the ORES damaging model, which has not been trained in a long time and has limited language support.
Goals
- Reduce moderation backlogs by preventing bad edits from entering patroller queues.
- Give moderators confidence that automoderation is reliable and is not producing significant false positives.
- Ensure that editors caught in a false positive have clear avenues to flag the error / have their edit reinstated.
- Are there other goals we should consider?
Design research
We delved into a comprehensive design research process to establish a strong foundation for the configuration tool for Automoderator. At the core of our approach is the formulation of essential design principles for shaping an intuitive and user-friendly configuration interface.
We looked at existing technologies and best practices and this process is known as desk research. This allowed us to gain valuable insights into current trends, potential pitfalls, and successful models within the realm of automated content moderation. We prioritized understanding the ethical implications of human-machine learning interaction, and focused on responsible design practices to ensure a positive and understandable user experience. We honed in on design principles that prioritize transparency, user empowerment, and ethical considerations.
Model
This project will leverage the new revert risk models developed by the Wikimedia Foundation Research team. There are two versions of this model:
- A multilingual model, with support for 47 languages.
- A language-agnostic model.
These models can calculate a score for every revision denoting the likelihood that the edit should be reverted. We envision providing communities with a way to set a threshold for this score, above which edits would be automatically prevented or reverted.
The models currently only support Wikipedia, but could be trained on other Wikimedia projects. Additionally they are currently only trained on the main (article) namespace. Once deployed, we could re-train the model on an ongoing basis as false positives are reported by the community.
Before moving forward with this project we would like to provide opportunities for testing out the model against recent edits, so that patrollers can understand how accurate the model is and whether they feel confident using it in the way we're proposing.
- Do you have any concerns about these models?
- What percentage of false positive reverts would be the maximum you or your community would accept?
Potential solution
We are envisioning a tool which could be configured by a community's moderators to automatically prevent or revert edits. Reverting edits is the more likely scenario - preventing an edit requires high performance so as not to impact edit save times. Additionally, it provides less oversight of what edits are being prevented, which may not be desirable, especially with respect to false positives. Moderators should be able to configure whether the tool is active or not, have options for how strict the model should be, determine the localised username and edit summary used, and more.
Lower thresholds would mean more edits get reverted, but the false positive rate is higher, while a high threshold would revert a smaller number of edits, but with higher confidence.
While the exact form of this project is still being explored, the following are some feature ideas we are considering, beyond the basics of preventing or reverting edits which meeting a revert risk threshold.
Testing
If communities have options for how strict they want the automoderator to be, we need to provide a way to test those thresholds in advance. This could look like AbuseFilter’s testing functionality, whereby recent edits can be checked against the tool to understand which edits would have been reverted at a particular threshold.
- How important is this kind of testing functionality for you? Are there any testing features you would find particularly useful?
Community configuration
A core aspect of this project will be to give moderators clear configuration options for setting up the automoderator and customising it to their community’s needs. Rather than simply reverting all edits meeting a threshold, we could, for example, provide filters for not operating on editors with certain user groups, or avoiding certain pages.
- What configuration options do you think you would need before using this software?
False positive reporting
Machine learning models aren't perfect, and so we should expect that there will be a non-zero number of false positive reverts. There are at least two things we need to consider here: the process for a user flagging that their edit was falsely reverted so it can be reinstated, and providing a mechanism for communities to provide feedback to the model over time so that it can be re-trained.
The model is more sensitive to edits from new and unregistered users, as this is where most vandalism comes from. We don't want this tool to negatively impact the experience of good faith new users, so we need to create clear pathways for new users to understand that their edit has been reverted, and be able to reinstate it. This needs to be balanced with not providing easy routes for vandals to undo the tool's work, however.
Although these models have been trained on a large amount of data, false positive reporting by editors can provide a valuable dataset for ongoing re-training of the model. We need to figure out how to enable experienced editors to send false positive data back to the model so that it can improve over time.
- How could we provide clear information and actions for editors on the receiving end of a false positive, in a way which isn’t abused by vandals?
- What concerns do you have about false positives?
Designs
Our current plans for Automoderator have two UI components:
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Landing page.
A landing page with information about Automoderator, a way to appeal the bot’s decisions, and a link to configure the bot.
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Configuration page. -
Making changes to the configuration page. -
Saving changes to the configuration page.
The configuration page, which will be generated by Community Configuration. In the MVP, admins will be able to turn Automoderator on or off, configure its threshold (i.e. how it should behave), and customize its default edit summary and username. We anticipate that we'll add more configuration options over time in response to feedback. Once the page is saved, if the user has turned Automoderator on, it will start running immediately.
Other open questions
- If your community uses a volunteer-maintained anti-vandalism bot, what has your experience of that bot been? How would you feel if it stopped working?
- Do you think your community would use this? How would it fit in with your other workflows and tools?
- What else should we consider that we haven't documented above?