How does Facebook's automated detection system detect duplicate or similar ads?

Facebook's automated detection system uses a variety of signals and techniques to detect duplicate or similar ads on the platform.

One approach is to use image recognition algorithms to compare the visual content of different ads. The system can analyze the images used in each ad, identifying similarities and differences between them. This can help to detect duplicate or very similar ads that use slightly different text or targeting criteria.

The system can also analyze the text content of each ad, using natural language processing (NLP) techniques to identify common phrases, keywords, and themes. This can help to detect ads that use similar language or messaging, even if they use different images or targeting criteria.

Another approach is to analyze the targeting criteria used in each ad, identifying patterns and similarities between different ads. For example, if multiple ads are targeting the same audience or using similar interests or behaviors, this may indicate that they are duplicate or similar ads.

The system can also use machine learning algorithms to identify patterns and trends in ad content and targeting, allowing it to detect emerging threats and policy violations.

Overall, Facebook's automated detection system uses a combination of image recognition, NLP, and machine learning techniques to detect duplicate or similar ads on the platform, helping to ensure compliance with its policies and prevent spam or fraudulent activity.