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Content moderation is the job of monitoring user-generated web content to make sure that nothing inappropriate, offensive, or disturbing gets published online. Things like false advertising, fraud, or sexually explicit images can seriously harm the reputation of an online business, whether it’s an e-commerce marketplace, an AdTech platform, or a social media site.
As online content grows exponentially, keeping the web safe is no easy task. Companies today use Machine Learning models to build their automated moderation systems. These systems rely heavily on high-quality, accurately labeled data for training and validating their models — and this is where crowdsourcing can play an invaluable role.
Yandex Zen is a popular recommendation service that provides user-generated content and uses ML algorithms to support automated moderation. The service takes the form of a personalized news feed that shows articles and videos from external digital media sources, as well as original pieces from its own blog. The website has 20 million daily active users, including 5000 daily new authors, the most popular of whom are able to monetize their contributions.
Since its launch in 2015, Yandex Zen has encountered 2 major issues:
The platform relies on human moderators to cover the grey areas that their ML algorithms can’t handle reliably, as well as to monitor trending content in real time. The team found it was a challenge to keep enough moderators online 24/7, label enough content to train and validate their ML models, and maintain quality of moderation.
Toloka was asked to utilize the power of the crowd and help improve moderation at Yandex Zen. Moderators were expected to identify the following types of violations:
Toloka’s Crowd Solution Architects (CSAs) isolated 3 classification formats for the project – text, image, and video.
Normally, majority vote is used to improve quality on classification tasks. This means that each item is classified by multiple Tolokers and results are automatically aggregated to use the most popular verdict. However, this moderation project was a long-running continuous process, and majority vote revealed socio-demographic bias that degraded quality over time.
In addition, the website’s fast-paced environment with freshly published hourly content meant that moderation and control tasks had to be regular and frequent, while all trending content required real-time moderation.
Toloka created 10 projects for articles and videos, as well as 4 projects for the comment section. Each project contained around 10,000 daily crowdsourcing tasks.
The Yandex Zen team came up with a new infrastructure for moderating content efficiently. Initially, the majority of content is handled by automatic classifiers. Anything that falls into a gray area is passed on to the Toloka crowd for classification. This crowd is supported by in-house moderators, who verify the performers’ work and help control quality. A handful of expert moderators create control tasks with true labels, which are used for daily secret exams to monitor quality and reward those who perform well. In-house moderators can focus on trending content that demands careful consideration, while letting the crowd handle the majority of tasks.
Here’s what the new collaborative process achieves:
As a result, moderation accuracy has risen significantly, reaching figures up to 98 percent. Content classification quality also improved in the first 2,5 months of working together with Toloka.