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We'll talk about how crowdsourcing helps the Yandex support team on social media.
Social media monitoring is no news: the first services that were catching online mentions appeared as much as a decade ago. But there is still a problem: how can we automate mention filtering to only read what's important and ignore whatever is irrelevant?
If a company is mentioned not that much, say, up to ten mentions a day, you can filter them yourself. This usually happens like this: you read a mention, analyze it, and make a decision.
If it's important, you show it to the Customer Care team, if not, you mark it as insignificant in the monitoring system. If there are several hundred mentions a day, you'll need a staff of assistants. But a company might be mentioned thousands of times a day, mostly in passing or to draw attention to a post.
Yandex is mentioned in social media more than 80,000 times a day. That is, every second something is written on the web about Yandex or some of its services. Our goal is to analyze this flow within 15 minutes. A quarter of an hour after the content gets into the monitoring system, the social media communication service operators should receive mentions that need to be responded to.
This analysis requires an average of about 30 full-time employees. This is expensive and not very efficient, so we built our own system for filtering mentions. At the heart of this system is the Toloka crowdsourcing platform. Over 40,000 Tolokers complete simple tasks and earn money everyday. For example, the task can be to identify what is shown in an image, choose the most suitable option from multiple ones, or go to a specific location and take a picture of a certain object.
Filtering mentions in general is too difficult a task for tolokers. But it can be decomposed into a number of simple tasks: to identify the service, to evaluate the tone (positive, negative, or neutral), and to determine the importance of a message. Tolokers can accomplish tasks like this if they're given short and clear instructions.
All references are divided into three groups:
Here's how this task looks for a Toloker:
Often this question can be answered automatically. Often, but not always.
What analytics are they talking about? This could be Yandex.Drive, Yandex.Disk, Yandex.Direct. Here the context will help the toloker, such as the name of the chat or the topic of discussion. By the way, the correct answer in this case is Yandex.Direct.
Mentions can be positive, neutral, or negative. Social media monitoring systems can determine the tone automatically, but do not always do this correctly. Here's an example:
The monitoring system will treat the first message as negative and the second one as positive. However, it's not the news itself that is important, but the attitude of users to it, so in fact the tone of both messages is neutral. Here's another example:
The monitoring system will mark this mention as negative. However, tolokers are human beings and they understand that the person is unhappy with the traffic conditions rather than the service performance.
To make sure that mentions are filtered efficiently, you need to train and control tolokers.
Tolokers work around the clock, so filtering is going on day and night. If things are calm on social media, they will do other tasks. And when a storm begins and the number of mentions increases dramatically, you simply involve more tolokers and mentions are still filtered within the same 15 minutes.
A tweet that once caused a very heated discussion: