Minimise targeted ads on social media
Social media platforms build profiles of us to better target us with ads. Learn how to minimise targeting and make this data collection useless!
We, like you, are fed up with how companies exploit data to target ads online. We’ve filed legal complaints over it, campaigned against it, called out empty promises, written letters, made gifs, formed alliances, and more. And we will continue this fight – but we need your help.
PI is often asked how to stop companies from targeting online ads at them. Every day there are stories about how random political actors and interest groups are attempting to influence people’s thinking through online advertising. Social media platforms play a key role in targeting ads at you – and they facilitate the use of data in ways that you probably wouldn’t like.
There are steps you can take to minimise all of this – and by doing so, show companies that you don’t want to be targeted with ads. These explainers show you step-by-step how to reduce the ways in which companies are able to target you with ads.*
At PI we think strong privacy settings and minimal collection of data should be the default. We’re working to make sure that the way data is used by political actors and advertisers does not facilitate the breakdown of democratic structures – and social platforms are important players in this work.
Taking these steps will not solve everything. Data will still be collected by companies and will still be used in advertising. But these steps can help making the advertising less targeted, meaning that an advertiser, in theory, will knows less about you. On top of these recommendations, there are different tools you can use to prevent the tracking of your online activity such as ad blockers. These steps are a start – and it’s worth doing.
Ad targeting
Ads are "targeted" when they are aimed at an audience with specific traits based on the product or service that is being advertised.
Ads personalisation
Ads are "personalised" when they are targeted to a specific person based on their perceived or inferred interests or characteristics. These interests and characteristics are themselves derived from previous online activity, such as visited websites or apps used.
