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On Facebook and foreign propaganda: Here’s how algorithms can manipulate you – Rappler

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An internal Facebook report found that the social media platform’s algorithms – the rules its computers follow in deciding the content that you see – enabled disinformation campaigns based in Eastern Europe to reach nearly half of all Americans in the run-up to the 2020 presidential election, according to a report in Technology Review.

The campaigns produced the most popular pages for Christian and Black American content, and overall reached 140 million U.S. users per month. Seventy-five percent of the people exposed to the content hadn’t followed any of the pages. People saw the content because Facebook’s content-recommendation system put it into their news feeds.

Social media platforms rely heavily on people’s behavior to decide on the content that you see. In particular, they watch for content that people respond to or “engage” with by liking, commenting and sharing. Troll farms, organizations that spread provocative content, exploit this by copying high-engagement content and posting it as their own.

As a computer scientist who studies the ways large numbers of people interact using technology, I understand the logic of using the wisdom of the crowds in these algorithms. I also see substantial pitfalls in how the social media companies do so in practice.

From lions on the savanna to Likes on Facebook
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Throughout millions of years of evolution, these principles have been coded into the human brain in the form of cognitive biases that come with names like familiaritymere exposure and bandwagon effect. If everyone starts running, you should also start running; maybe someone saw a lion coming and running could save your life. You may not know why, but it’s wiser to ask questions later.

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Your brain picks up clues from the environment – including your peers – and uses simple rules to quickly translate those signals into decisions: Go with the winner, follow the majority, copy your neighbor. These rules work remarkably well in typical situations because they are based on sound assumptions. For example, they assume that people often act rationally, it is unlikely that many are wrong, the past predicts the future, and so on.

Technology allows people to access signals from much larger numbers of other people, most of whom they do not know. Artificial intelligence applications make heavy use of these popularity or “engagement” signals, from selecting search engine results to recommending music and videos, and from suggesting friends to ranking posts on news feeds.

Not everything viral deserves to be

Our research shows that virtually all web technology platforms, such as social media and news recommendation systems, have a strong popularity bias. When applications are driven by cues like engagement rather than explicit search engine queries, popularity bias can lead to harmful unintended consequences.

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Social media like Facebook, Instagram, Twitter, YouTube and TikTok rely heavily on AI algorithms to rank and recommend content. These algorithms take as input what you like, comment on and share – in other words, content you engage with. The goal of the algorithms is to maximize engagement by finding out what people like and ranking it at the top of their feeds.

On the surface this seems reasonable. If people like credible news, expert opinions and fun videos, these algorithms should identify such high-quality content. But the wisdom of the crowds makes a key assumption here: that recommending what is popular will help high-quality content “bubble up.”

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We tested this assumption by studying an algorithm that ranks items using a mix of quality and popularity. We found that in general, popularity bias is more likely to lower the overall quality of content. The reason is that engagement is not a reliable indicator of quality when few people have been exposed to an item. In these cases, engagement generates a noisy signal, and the algorithm is likely to amplify this initial noise. Once the popularity of a low-quality item is large enough, it will keep getting amplified.

Algorithms aren’t the only thing affected by engagement bias – it can affect people too. Evidence shows that information is transmitted via “complex contagion,” meaning the more times people are exposed to an idea online, the more likely they are to adopt and reshare it. When social media tells people an item is going viral, their cognitive biases kick in and translate into the irresistible urge to pay attention to it and share it.

We recently ran an experiment using a news literacy app called Fakey. It is a game developed by our lab, which simulates a news feed like those of Facebook and Twitter. Players see a mix of current articles from fake news, junk science, hyperpartisan and conspiratorial sources, as well as mainstream sources. They get points for sharing or liking news from reliable sources and for flagging low-credibility articles for fact-checking.

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The wisdom of the crowds fails because it is built on the false assumption that the crowd is made up of diverse, independent sources. There may be several reasons this is not the case.

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First, because of people’s tendency to associate with similar people, their online neighborhoods are not very diverse. The ease with which social media users can unfriend those with whom they disagree pushes people into homogeneous communities, often referred to as echo chambers.

Second, because many people’s friends are friends of one another, they influence one another. A famous experiment demonstrated that knowing what music your friends like affects your own stated preferences. Your social desire to conform distorts your independent judgment.

Third, popularity signals can be gamed. Over the years, search engines have developed sophisticated techniques to counter so-called “link farms” and other schemes to manipulate search algorithms. Social media platforms, on the other hand, are just beginning to learn about their own vulnerabilities.

A different, preventive approach would be to add friction. In other words, to slow down the process of spreading information. High-frequency behaviors such as automated liking and sharing could be inhibited by CAPTCHA tests or fees. Not only would this decrease opportunities for manipulation, but with less information people would be able to pay more attention to what they see. It would leave less room for engagement bias to affect people’s decisions.

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It would also help if social media companies adjusted their algorithms to rely less on engagement to determine the content they serve you. Perhaps the revelations of Facebook’s knowledge of troll farms exploiting engagement will provide the necessary impetus. – Rappler.com

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Updating Special Ad Audiences for housing, employment, and credit advertisers

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On June 21, 2022 we announced an important settlement with the US Department of Housing and Urban Development (HUD) that will change the way we deliver housing ads to people residing in the US. Specifically, we are building into our ads system a method designed to make sure the audience that ends up seeing a housing ad more closely reflects the eligible targeted audience for that ad.

As part of this agreement, we will also be sunsetting Special Ad Audiences, a tool that lets advertisers expand their audiences for ad sets related to housing. We are choosing to sunset this for employment and credit ads as well. In 2019, in addition to eliminating certain targeting options for housing, employment and credit ads, we introduced Special Ad Audiences as an alternative to Lookalike Audiences. But the field of fairness in machine learning is a dynamic and evolving one, and Special Ad Audiences was an early way to address concerns. Now, our focus will move to new approaches to improve fairness, including the method previously announced.

What’s happening: We’re removing the ability to create Special Ad Audiences via Ads Manager beginning on August 25, 2022.

Beginning October 12th, 2022, we will pause any remaining ad sets that contain Special Ad Audiences. These ad sets may be restarted once advertisers have removed any and all Special Ad Audiences from those ad sets. We are providing a two month window between preventing new Special Ad Audiences and pausing existing Special Ad Audiences to enable advertisers the time to adjust budgets and strategies as needed.

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For more details, please visit our Newsroom post.

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Impact to Advertisers using Marketing API on September 13, 2022

For advertisers and partners using the API listed below, the blocking of new Special Ad Audience creation will present a breaking change on all versions. Beginning August 15, 2022, developers can start to implement the code changes, and will have until September 13, 2022, when the non-versioning change occurs and prior values are deprecated. Refer below to the list of impacted endpoints related to this deprecation:

For reading audience:

  • endpoint gr:get:AdAccount/customaudiences
  • field operation_status

For adset creation:

  • endpoint gr:post:AdAccount/adsets
  • field subtype

For adset editing:

  • endpoint gr:post:AdCampaign
  • field subtype

For custom audience creation:

  • endpoint gr:post:AdAccount/customaudiences
  • field subtype

For custom audience editing:

  • endpoint gr:post:CustomAudience

Please refer to the developer documentation for further details to support code implementation.

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Introducing an Update to the Data Protection Assessment

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Over the coming year, some apps with access to certain types of user data on our platforms will be required to complete the annual Data Protection Assessment. We have made a number of improvements to this process since our launch last year, when we introduced our first iteration of the assessment.

The updated Data Protection Assessment will include a new developer experience that is enhanced through streamlined communications, direct support, and clear status updates. Today, we’re sharing what you can expect from these new updates and how you can best prepare for completing this important privacy requirement if your app is within scope.

If your app is in scope for the Data Protection Assessment, and you’re an app admin, you’ll receive an email and a message in your app’s Alert Inbox when it’s time to complete the annual assessment. You and your team of experts will then have 60 calendar days to complete the assessment. We’ve built a new platform that enhances the user experience of completing the Data Protection Assessment. These updates to the platform are based on learnings over the past year from our partnership with the developer community. When completing the assessment, you can expect:

  • Streamlined communication: All communications and required actions will be through the My Apps page. You’ll be notified of pending communications requiring your response via your Alerts Inbox, email, and notifications in the My Apps page.

    Note: Other programs may still communicate with you through the App Contact Email.

  • Available support: Ability to engage with Meta teams via the Support tool to seek clarification on the questions within the Data Protection Assessment prior to submission and help with any requests for more info, or to resolve violations.

    Note: To access this feature, you will need to add the app and app admins to your Business Manager. Please refer to those links for step-by-step guides.

  • Clear status updates: Easy to understand status and timeline indicators throughout the process in the App Dashboard, App Settings, and My Apps page.
  • Straightforward reviewer follow-ups: Streamlined experience for any follow-ups from our reviewers, all via developers.facebook.com.

We’ve included a brief video that provides a walkthrough of the experience you’ll have with the Data Protection Assessment:

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The Data Protection Assessment elevates the importance of data security and helps gain the trust of the billions of people who use our products and services around the world. That’s why we are committed to providing a seamless experience for our partners as you complete this important privacy requirement.

Here is what you can do now to prepare for the assessment:

  1. Make sure you are reachable: Update your developer or business account contact email and notification settings.
  2. Review the questions in the Data Protection Assessment and engage with your teams on how best to answer these questions. You may have to enlist the help of your legal and information security points of contact to answer some parts of the assessment.
  3. Review Meta Platform Terms and our Developer Policies.

We know that when people choose to share their data, we’re able to work with the developer community to safely deliver rich and relevant experiences that create value for people and businesses. It’s a privilege we share when people grant us access to their data, and it’s imperative that we protect that data in order to maintain and build upon their trust. This is why the Data Protection Assessment focuses on data use, data sharing and data security.

Data privacy is challenging and complex, and we’re dedicated to continuously improving the processes to safeguard user privacy on our platform. Thank you for partnering with us as we continue to build a safer, more sustainable platform.

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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Resources for Completing App Store Data Practice Questionnaires for Apps That Include the Facebook or Audience Network SDK

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