Meta collects and infers information about you from other people's activity
Meta collects information about you based on what other people do, and also infers things about you from others' activity. Its examples: it might suggest a friend through Facebook's People You May Know or Instagram's Discover People if you both appear on a contact or connection list that someone uploads, and it takes into account whether your friends belong to a group when suggesting you join it. The linked examples of collection from others' activity are not supplied.
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We collect information about you based on others' activity.
We also infer things about you based on others' activity. For example:
We might suggest a friend to you through Facebook's People You May Know or Instagram’s Discover People if you both appear on a list of contacts or connections that someone uploads.
We take into account whether your friends belong to a group when we suggest you join it.
Qualifications & exceptions
Examples are illustrative; the full example note is not supplied.
Not stated here
Other kinds of information collected from others' activity beyond the examples.
Inference & Profiling · Company practice
Audience targeting example: Meta decides who fits an advertiser's audience from signals like Page likes
As an example of general demographics and interests, a bike shop with a Facebook Page can target people in Atlanta interested in cycling; Meta determines whether someone fits that audience based on signals such as liking a Page about bikes, and people in the audience could see the ad.
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We determine whether someone fits in this audience based on, for example, whether they liked a Page about bikes. Then people in that audience could see the bike shop's ad.
Inference & Profiling · Company practice
Ads system predicts your interests, matches advertiser audiences and ranks ads by machine learning
Meta's ads system uses your information to determine the ad interests most relevant to you, predicting them from the information it has including choices made in Ad preferences. Advertisers specify the audience they want, such as age, gender, location or interests. The system automatically reviews all potential ads where you might fit the advertiser's audience and decides which to show based on things like whether it best achieves the advertiser's goal and relevance to you, using algorithms and machine learning to order ads by factors such as ad quality, the advertiser's desired audience and budget, and how likely Meta thinks you are to react based on your activity on Meta Products. This process chooses the ad shown on Meta Company Products. A linked help page is not supplied.
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Our ads system works in the following way:
We use your information to determine the ad interests most relevant to you. We try to predict your ad interests based on the information we have, including any choices you've made in Ad preferences.
Advertisers tell us which audience they want to reach based on who they think will be most interested. This can include things like age, gender, location where advertisers want to show their ad or interests, such as women, based in Dublin, who like golf.
We review the potential ads that we could show you to identify the most relevant ones. Our ads system automatically reviews all the potential ads where you might fit the audience that advertisers want to reach. For example, you may see potential ads because you match the age range and interest category. We determine which ones to show you based on things like whether it best achieves the advertiser’s goal and is most relevant to you. To do this, we use algorithms and machine learning to order the ads we may show you. We use different factors such as the ad quality, the advertiser's desired audience and budget. We also consider how likely we think it is that you will react to the ad based on things like your activity on Meta Products.
All of this information helps us show you the most relevant ads. When you see an ad on Meta Company Products, we use this process to choose the most relevant ad to show you.
Qualifications & exceptions
Ad preferences choices feed the interest predictions.