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A group of moms on Facebook built an island of good-faith vaccine – The Washington Post

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“On both sides, there’s people telling the truth, at least their truth,” said Buchanan, 32, who last month became infected with the coronavirus. “But on the pro-vaccine side, there was just more logic” — and more links to solid research. “On the anti-vaccine side, there was more conspiracy.”

Now he’s going to get vaccinated.

As covid-19 cases surge in the United States, jeopardizing the reopening of schools and offices and rekindling debates about mask and vaccination mandates, the battle to win over the vaccine skeptical has taken on fresh urgency. Much of that struggle is happening on social media, where misinformation about the vaccines continues to flourish. Facebook, Twitter and YouTube have all established rules against posting false information about covid and vaccines. Yet just this weekend Facebook said the most-shared link on its site from January to March this year was an article that raised concerns that coronavirus vaccines could lead to death.

Amid the online scare stories and anti-vaccine memes, an army of local influencers and everyday users is waging a grass-roots campaign on Facebook, Reddit and other platforms to gently win over the vaccine skeptical. They’re spending hours moderating forums, responding to comments, linking to research studies, and sharing tips on how to talk to fearful family members.

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“It feels a lot like covid is something that is completely out of control and there is nothing we can do, like it’s this out-of-control wildfire, and I’m just one person with a little hose,” said Kate Bilowitz, an Oakland, Calif.-based mom who works for a real estate company and co-founded Vaccine Talk. “But when people reach out to us, it feels like it’s making a little bit of a difference.”

Their work exemplifies Facebook’s stated goal to “bring the world closer together.” But Bilowitz and others who run similar forums say that the interventions made by technology companies are often counterproductive, and that software algorithms frequently delete valuable conversations mistaken for misinformation.

“Facebook is attempting to shut down misinformation by shutting down all conversation entirely,” she said. “I strongly believe that civil, evidence-based discussion works, and Facebook’s policies make it extremely difficult for that to happen.”

Facebook leans on software and its army of 15,000 human moderators to detect covid misinformation. Last month, it said it had taken down more than 20 million posts since the start of the pandemic. But it routinely misses new memes and conspiracy theories, while at times scrubbing legitimate information. At the same time, its news feed algorithms boost posts that get the most engagement, often helping the most sensational claims go viral.

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Twitter and Google-owned YouTube have employed similar strategies, using algorithms to parse text posts and listen to videos, sometimes banning them immediately and other times flagging them for a human to review. The companies have peppered their sites with links to official coronavirus information from the Centers for Disease Control and Prevention and World Health Organization, as well as pushing articles from mainstream news organizations to the top of people’s feeds and search results.

President Biden on July 19 said that Facebook was not killing people, but that coronavirus vaccine misinformation on its site was killing people. (The Washington Post)

While the White House is launching a project to pay micro-influencers to spread pro-vaccine messaging, a handful of carefully designed and self-policed online spaces, such as the Vaccine Talk group on Facebook, are showing that it is possible to change people’s minds, one nuanced post at a time. To do it, they’re adopting moderation systems and rules of discourse very different from those of the social media platforms.

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People’s attitudes toward the coronavirus vaccines are complex, and it’s hard to quantify the role social media has played in sowing or overcoming their doubts. A recent survey by Rutgers University found that people who get their news primarily from Facebook were less likely to be vaccinated than any other group of news consumers.

But research also supports the idea that social media can be a force for pro-vaccine persuasion, under the right circumstances. A forthcoming study from researchers at the MIT Sloan School of Management, which has not yet been peer-reviewed, found that people on the fence about vaccination can be swayed by learning that others around them are getting vaccinated. And a 2020 study by health misinformation researchers Emily Vraga of the University of Minnesota and Leticia Bode of Georgetown University showed that social media users who rebut misleading claims with factual information may not persuade the original poster but can influence the beliefs of others who witness the exchanges.

People concerned about vaccine safety may be easier to convince than those who don’t trust the government or medical authorities, said Wendy King, an associate professor in the Department of Epidemiology at the University of Pittsburgh Graduate School of Public Health. Earlier this year, King surveyed more than 5 million U.S. adults about their attitudes toward coronavirus vaccines. Many who said they may not or won’t get vaccinated said they feared side effects — a sign they may be influenced by misinformation.

“They are reading a lot of nonscientific literature and a lot of social media posts about things that are happening to people post-vaccine, and they are really worried,” King said.

That’s what happened for Bilowitz, 35. She first turned to Facebook for information on childhood vaccinations in 2015, after she gave birth. Months earlier, there had been a measles outbreak at Disneyland, which officials blamed on too-low vaccination rates. She realized a Facebook group she had joined was run by vaccine opponents, part of the anti-vaccine movement that flourished on social media long before the coronavirus pandemic.

After Bilowitz and some other moms got kicked out of that group, they formed the Vaccine Talk group to focus on evidence-based information.

“The most important rule was ‘civility,’ ” Bilowitz said. “There are some groups online where people just yell at each other. We wanted to just be able to talk to one another without it getting that way.”

Vaccine Talk now has nearly 70,000 members, each of whom must gain administrators’ approval to join and commit to a code of conduct. Strict rules prohibit users from misrepresenting themselves, offering medical advice and harassing or bullying people. Another key rule: Be ready to provide citations within 24 hours for any claim you make. Twenty-five moderators and administrators in six countries monitor the posts, and those who flout the rules are kicked out.

“Usually, the hardcore anti-vaxxers cannot follow the rules,” Bilowitz said. “They are usually spamming people with their commentary. I think it’s hard for them: They are basically coming out of an echo chamber.”

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Vaccine Talk represents exactly the type of conversations Facebook says it wants to cultivate. But Bilowitz said the social network’s often clumsy and heavy-handed enforcement of covid misinformation policies has made their work more difficult. In June, Facebook temporarily shut down the group because someone posted an article deemed to be misinformation. But the poster had been seeking advice on how to rebut the article.

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“We were just caught up in the algorithm,” Bilowitz said, “and felt there wasn’t a human in charge of the process.”

To combat covid misinformation, Facebook has created both a banner across its site and a tab on covid-related posts that link people to authoritative information from public health organizations. The company’s head of health, Kang-Xing Jin, said surveys suggest vaccine acceptance on the part of American Facebook users has increased since January, from about 70 percent to upward of 80 percent. But he has also acknowledged the challenge in drawing a line between posts that evince earnest skepticism and those that are ideologically motivated.

Monica Buescher, a 32-year-old teacher in Vacaville, Calif., said she went “deep down the rabbit hole” of anti-vaccine misinformation when she had her second child in 2019. Convinced that shots were dangerous, she nonetheless wanted to hear the pro-vaccine side. She found her way to Vaccine Talk, which she said had a reputation among anti-vaccine groups as being “mean” for banning those who made claims without scientific evidence.

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On Vaccine Talk, Buescher credits a handful of people with walking her through the scientific evidence and persuading her that routine childhood vaccines are safe and effective. Now, Buescher is helping her friends and family navigate conflicting information about coronavirus vaccines.

“It’s mostly just offering my experience, hopefully in an unassuming way that implies I’ve tried to understand both sides,” she said.

Vaccine Talk isn’t the only venue for such conversations. In other corners of the Internet where ground rules and moderators facilitate conversations that don’t spiral into shouting matches, people who had avoided getting vaccinated against the coronavirus are finding reasons to do so.

Ryan Steward, 29, a mechanic and pastor from Spartanburg, S.C., had never hesitated to get vaccines for himself or his family — until he started hearing scary stories about the covid shots.

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“I wanted to find a way to break out of my echo chambers,” he said, “find something to shake me out of this fear that had been instilled in me by misinformation and by the media.”

A frequent user of automotive forums on the social media site Reddit, Steward was aware of a popular group called “r/ChangeMyView,” where people post a controversial opinion and invite others to challenge their assumptions. Those who succeed are awarded points, a system meant to incentivize users to keep their replies civil and constructive.

On Aug. 8, Steward posted there for the first time, spelling out his concerns about coronavirus vaccines. He cited the lack of full Food and Drug Administration approval (which came on Monday), the potential for long-term side effects and reports of breakthrough cases among the vaccinated — and braced himself for a cavalcade of vitriol. But aside from one nasty comment that was quickly deleted by the group’s moderators, he got the opposite.

“I understand where you’re coming from,” wrote one user. “The same concerns crossed my mind. However, you aren’t just deciding on the risks of the vaccine, or not. You’re deciding on the risks of the vaccine or the risks of covid.” Other commenters explained the FDA approval process, linked to academic studies and pointed to the pro-vaccine consensus among doctors worldwide, not just in the United States.

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Steward felt grateful — and convinced.

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“Within a matter of two hours, my mind had been changed completely,” he said. He showed the thread to his wife, who agreed to get vaccinated if he went first. So Steward scheduled an appointment — and received his first Moderna shot on Aug. 13.

In interviews, two moderators of ChangeMyView said they see their forum as an oasis of polite discourse in a digital landscape dominated by shouting matches. They also said it’s a constant struggle, on the part of more than 20 active moderators and a core of conscientious users, to keep it that way.

If you want to persuade someone, “approaching them with a sense of empathy is very important — and very much missing, I think, from a huge amount of conversation that happens online,” said London entrepreneur Stuart Johnson, 20, one of the subreddit’s active moderators. “Even if you’re completely right, if your post is just strongly calling out the other person or generalizing about them, they will completely close down. Their ego will no longer allow them to change their mind.”

Johnson also acknowledged, however, that the person has to be open to having their mind changed in the first place. Surveys suggest that many vaccine holdouts are more likely to be persuaded by mandates or cash payments than online debate.

The big tech companies have also made significant changes to their moderation policies during the pandemic. Last year, Facebook, Twitter and YouTube all said that posts promoting fake treatments for covid would be taken down. And they’ve worked with the Centers for Disease Control and Prevention and World Health Organization to append links to authoritative information on any post they deem to be about covid or vaccines. YouTube also has begun working with hospital groups to create new video series that offer authoritative medical information in response to popular health-related searches.

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Yet online misinformation has proved resilient. A study published earlier this month by researchers at Yale University found that Twitter users learn over time to tweet more “expressions of moral outrage” when such tweets generate more likes and retweets. “Even if platform designers do not intend to amplify moral outrage,” the authors wrote, “design choices aimed at satisfying other goals such as profit maximization via user engagement can indirectly affect moral behavior because outrage-provoking content draws high engagement.”

Outside these purpose-built forums, pro-vaccine messages from everyday Americans can also have an impact. Chelsey Palmer, a 32-year-old lawyer from Jacksonville, Fla., held off getting vaccinated until late June — first because she was pregnant and then because “covid seemed like it died down a little bit.”

But in recent weeks, as the delta variant spread, she saw appeals from local medical professionals in her Facebook feed describing worsening conditions in hospitals.

“Seeing people who were here locally in Jacksonville, just sharing their own experiences of what was going on, seeing their firsthand experience, really gave me the push I needed to get” vaccinated, Palmer said.

In general, Palmer does not see Facebook as a good place to go for vaccine information, however.

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“For the average person who’s not really trying to use critical thinking on social media, it’s actually a pretty dangerous place,” she said, noting that her feed is also riddled with scaremongering and conspiracy theories.

For those debating whether to get vaccinated, “I would not recommend going to social media,” Palmer said. “I would recommend going to a doctor.”

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

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Updated July 18: Developers and advertising partners may be required to share information on their app’s privacy practices in third party app stores, such as Google Play and the Apple App Store, including the functionality of SDKs provided by Meta. To help make it easier for you to complete these requirements, we have consolidated information that explains our data collection practices for the Facebook and Audience Network SDKs.

Facebook SDK

To provide functionality within the Facebook SDK, we may receive and process certain contact, location, identifier, and device information associated with Facebook users and their use of your application. The information we receive depends on what SDK features 3rd party applications use and we have structured the document below according to these features.

App Ads, Facebook Analytics, & App Events

Facebook App Events allow you to measure the performance of your app using Facebook Analytics, measure conversions associated with Facebook ads, and build audiences to acquire new users as well as re-engage existing users. There are a number of different ways your app can use app events to keep track of when people take specific actions such as installing your app or completing a purchase.

With Facebook SDK, there are app events that are automatically logged (app installs, app launches, and in-app purchases) and collected for Facebook Analytics unless you disable automatic event logging. Developers determine what events to send to Facebook from a list of standard events, or via a custom event.

When developers send Facebook custom events, these events could include data types outside of standard events. Developers control sending these events to Facebook either directly via application code or in Events Manager for codeless app events. Developers can review their code and Events Manager to determine which data types they are sending to Facebook. It’s the developer’s responsibility to ensure this is reflected in their application’s privacy policy.

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Advanced Matching

Developers may also send us additional user contact information in code, or via the Events Manager. Advanced matching functionality may use the following data, if sent:

  • email address, name, phone number, physical address (city, state or province, zip or postal code and country), gender, and date of birth.
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Facebook Login

There are two scenarios for applications that use Facebook Login via the Facebook SDK: Authenticated Sign Up or Sign In, and User Data Access via Permissions. For authentication, a unique, app-specific identifier tied to a user’s Facebook Account enables the user to sign in to your app. For Data Access, a user must explicitly grant your app permission to access data.

Note: Since Facebook Login is part of the Facebook SDK, we may collect other information referenced here when you use Facebook Login, depending on your settings.

Device Information

We may also receive and process the following information if your app is integrated with the Facebook SDK:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, CPU core, storage size, SDK version, OS and app versions, and app package name; and
  • Networking information, such as the name of the mobile operator or ISP, language, time zone, and IP address.

Audience Network SDK

We may receive and process the following information when you use the Audience Network SDK to integrate Audience Network ads in your app:

  • Device identifiers;
  • Device attributes, such as device model and screen dimensions, operating system, mediation platform and SDK versions; and
  • Ad performance information, such as impressions, clicks, placement, and viewability.

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Enabling Faster Python Authoring With Wasabi

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This article was written by Omer Dunay, Kun Jiang, Nachi Nagappan, Matt Bridges and Karim Nakad.


Motivation

At Meta, Python is one of the most used programming languages in terms of both lines of code and number of users. Everyday, we have thousands of developers working with Python to launch new features, fix bugs and develop the most sophisticated machine learning models. As such, it is important to ensure that our Python developers are productive and efficient by giving them state-of-the-art tools.

Introducing Wasabi

Today we introduce Wasabi, a Python language service that implements the language server protocol (LSP) and is designed to help our developers use Python easier and faster. Wasabi assists our developers to write Python code with a series of advanced features, including:

  • Lints and diagnostics: These are available as the user types.
  • Auto import quick fix: This is available for undefined-variable lint.
  • Global symbols autocomplete: When a user types a prefix, all symbols (e.g. function names, class names) that are defined in other files and start with that prefix will appear in the autocomplete suggestion automatically.
  • Organize Imports + Remove unused: A quick fix that removes all unused imports and reformats the import section according to pep8 rules. This feature is powered by other tools that are built inside Meta such as libCST that helps with safe code refactoring.
  • Python snippets: Snippet suggestions are available as the user types for common code patterns.

Additionally, Wasabi is a surface-agnostic service that can be deployed into multiple code repositories and various development environments (e.g., VSCode, Bento Notebook). Since its debut, Wasabi has been adopted by tens of thousands of Python users at Meta across Facebook, Instagram, Infrastructure teams and many more.

Figure 1: Example for global symbols autocomplete, one of Wasabi’s features

Language Services at Meta Scale

A major design requirement for language services is low latency / user responsiveness. Autocomplete suggestions, lints and quickFixes should appear to the developer immediately as they type.

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At Meta, code is organized in a monorepo, meaning that developers have access to all python files as they develop. This approach has major advantages for the developer workflow including better discoverability, transparency, easier to share libraries and increased collaboration between teams. It also introduces unique challenges for building developer tools such as language services that need to handle hundreds of thousands of files.

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The scaling problem is one of the reasons that we tried to avoid using off-the-shelf language services available in the industry (e.g., pyright, jedi) to perform those operations. Most of those tools were built in the mindset of a relatively small to medium workspace of projects, maybe with the assumptions of thousands of files for large projects for operations that require o(repo) information.

For example, consider the “auto import” quick fix for undefined variables. In order to suggest all available symbols the language server needs to read all source files, the quick fix parses them and keeps an in-memory cache of all parsed symbols in order to respond to requests.

While this may scale to be performed in a single process on the development machine for small-medium repositories, this approach doesn’t scale in the monorepo use case. Reading and parsing hundreds of thousands of files can take many minutes, which means slow startup times and frustrated developers. Moving to an in-memory cache might help latency, but also may not fit in a single machine’s memory.

For example, assume an average python file takes roughly 10ms to be parsed and to extract symbols in a standard error recoverable parser. This means that on 1000 files it can take 10 seconds to initialize which is a fairly reasonable startup time. Running it on 1M files would take 166 minutes which is obviously a too lengthy startup time.

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How Wasabi Works

Offline + Online Processing:

In order to support low latency in Meta scale repositories, Wasabi is powered by two phases of parsing, background processing (offline) done by an external indexers, and local processing of locally changed “dirty files” (online):

  1. A background process indexes all committed source files and maintains the parsed symbols in a special database (glean) that is designed for storing code symbol information.
  2. Wasabi, which is a local process running on the user machine, calculates the delta between the base revision, stack of diffs and uncommitted changes that the user currently has, and extracts symbols only out of those “dirty” files. Since this set of “dirty” files is relatively small, the operation is performed very fast.
  3. Upon an LSP request such as auto import, Wasabi parses the abstract syntax tree (AST) of the file, then based on the context of the cursor, creates a query for both glean and local changes symbols, merges the results and returns it to the user.
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As a result, all Wasabi features are low latency and available to the user seamlessly as they type.

Note: Wasabi currently doesn’t handle the potential delta between the revision that glean indexed (happens once every few hours) and the locally base revision that the user currently has. We plan on adding that in the future.

Figure 2: Wasabi’s high level architecture

Ranking the Results

In some cases, due to the scale of the repository, there may be many valid suggestions in the set of results. For example, consider “auto import” suggestions for the “utils” symbol. There may be many modules that define a class named “utils” across the repository, therefore we invest in ranking the results to ensure that users see the most relevant suggestions on the top.

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For example, auto import ranking is done by taking into account:

  • Locality:
    • The distance of the suggested module directory path from the directory paths of modules that are already imported in this file.
    • The distance of the suggested module directory path from the current directory path of the local file.
    • Whether the file has been locally changed (“dirty” files are ranked higher).
  • Usage: The number of occurrences the import statement was used by other files in the repository.

To measure our success, we measured the index in the suggestion list of an accepted suggestion and noted that in almost all cases the accepted suggestion was ranked in one of top 3 suggestions.

Positive feedbacks from developers

After launching Wasabi to several pilot runs inside Meta, we have received numerous positive feedbacks from our developers. Here is one example of the quote from a software engineer at Instagram:

“I’ve been using Wasabi for a couple months now, it’s been a boon to my productivity! Working in Instagram Server, especially on larger files, warnings from pyre are fairly slow. With Wasabi, they’re lightning fast 😃!”

“I use features like spelling errors and auto import several times an hour. This probably makes my development workflow 10% faster on average (rough guess, might be more, definitely not less), a pretty huge improvement!”

As noted above, Wasabi has made a meaningful change to keep our developers productive and make them feel delightful.

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The metric to measure authoring velocity

In order to quantitatively understand how much value Wasabi has delivered to our Python developers, we have considered a number of metrics to measure its impact. Ultimately, we landed on a metric that we call ‘Authoring Velocity’ to measure how fast developers write code. In essence, Authoring Velocity is the inverse function of the time taken on a specific diff (a collection of code changes) during the authoring stage. The authoring stage starts from the timestamp when a developer checks out from the source control repo to the timestamp when the diff is created. We have also normalized it against the number of lines of code changed in the diff, as a proxy for diff size, to offset any possible variance. The greater the value for ‘Authoring Velocity,’ the faster we think developers write their code.

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Figure 3: Authoring Velocity Metric Formula

The result

With the metric defined, we ran an experiment to measure the difference that Wasabi brings to our developers. Specifically, we selected ~700 developers who had never used Wasabi before, and then randomly put them into two independent groups at a 50:50 split ratio. For these developers in the test group, they were enabled with Wasabi when they wrote in Python, whereas there was no change for those in the control group. For both groups, we compare the changes in relative metric values before and after the Wasabi enablement. From our results, we find that for developers in the test group, the median value of authoring velocity has increased by 20% after they started using Wasabi. Meanwhile, we don’t see any significant change in the control group before and after, which is expected.

Figure 4: Authoring Velocity measurements for control and test groups, before and after Wasabi was rolled out to the test group.

Summary

With Python’s unprecedented growth, it is an exciting time to be working in the area to make it better and handy to use. Together with its advanced features, Wasabi has successfully improved developers’ productivity at Meta, allowing them to write Python faster and easier with a positive developer experience. We hope that our prototype and findings can benefit more people in the broader Python community.

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To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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