In the third step, we determined that there are some actions most often performed by the authors of the ads. It may be called a set of typical edits.
One of the most common edits is a price change without changing the text of the ad. This edit cannot affect the compliance with the advertising law, so we gave users the opportunity to make such changes without re-moderation. So, we did with a number of attributes that logically did not require moderation.
Fourth. In the fourth step, we added a system for parsing text and highlighting certain information in it. We did it this way: we taught the system to analyze the ads published by users. On the one hand, it highlights useful information — for example, addresses and other details. On the other hand, it checks keywords that indicate that this is an illegal ad.
If the system could recognize the ad and did not find anything suspicious, then it was not sent for moderation, but automatically published. If something was found or was not recognized, then the ad was sent to the moderator. Unrecognized ads were used for further training of our AI.
Here elements of artificial intelligence with machine learning are used. As a result, our AI successfully found in the new ads attempts to deceive the system, for example, when the header contained allowed information, but text of ad included something illegal.