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Mountain Valley Pipeline seeks the identity of its anonymous critics | Local News | roanoke.com

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Mountain Valley Pipeline seeks the identity of its anonymous critics


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MVP steep slopes

An aerial view shows a section of land being prepared for the Mountain Valley Pipeline, stopping short of a creek.

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The Roanoke Times, File May


Very little is publicly known about a very public critic of the Mountain Valley Pipeline.

Appalachians Against Pipelines established a Facebook page in February 2018, about the time that tree-sitters began their efforts to block construction of the massive natural gas pipeline.

Since then, the group has used social media as a megaphone to promote its agenda, while otherwise remaining largely invisible.

Mountain Valley is trying to find out who they are. In a subpoena recently filed in Roanoke’s federal court, the company asks Facebook to reveal the names and telephone numbers of those who established and maintain a page that has more than 21,000 followers.

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Appalachians Against Pipelines says the subpoena is nothing more than an effort to intimidate and silence them — a position shared by the Electronic Frontier Foundation, a nonprofit group that advocates for privacy and free speech on the internet.

“It generally gives us concern when we see a company like MVP trying to unmask its critics,” said Adam Schwartz, a senior attorney with the San Francisco-based foundation.

The U.S. Supreme Court has recognized the rights of critics to remain unnamed, noting that the practice can be traced back to the founders of the nation who published newspaper pieces anonymously out of fear of retaliation from the King of England.

In order for a company like Mountain Valley to acquire the names of the administrators of a Facebook page, it must meet a high standard of showing that its needs or concerns outweigh the First Amendment rights of the commenters, Schwartz said.

“The right to stay anonymous is not absolute,” he said.

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Schwartz said it is not unheard of for a company to seek information about its opponents from the social media outlets they use. When a subpoena is contested, judges make case-by-case decisions.

Mountain Valley declined to comment Friday, saying that “details regarding any pending or potential litigation efforts cannot be provided at this time.”

The subpoena was filed Aug. 20 in connection with a pending case in U.S. District Court in Roanoke, where the company has been at odds for years with the owners of a Bent Mountain property through which the pipeline needs to pass.

Coles and Theresa “Red” Terry became concerned when construction crews began drilling through bedrock on their land last month in preparation for blasting to clear a path for the buried, 42-inch pipe.

They filed a request for an injunction to stop the blasting, saying that it threatened to contaminate their well water. The request was denied Aug. 13 by District Judge Elizabeth Dillon.

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One week later, Mountain Valley filed its own request for an injunction, asking Dillon to order that protesters of the pipeline not interfere with work on the Terry property.

“On Aug. 11, in order to prevent the blasting from proceeding, protesters were invited to the property to position themselves along the edge of the easements,” the motion states. “Messages were posted on social media asking readers to join the protest.”

The motion does not mention Appalachians Against Pipelines.

But in an Aug. 11 post to the group’s Facebook page, a “call for support” suggested that supporters of the Terrys show up at their property, and instructed them to the page of another group, Water Is Life Protect It, for directions.

The subpoenas issued to Facebook ask for the administrators of both groups’ pages. Efforts to reach Water is Life Protect It for this story were unsuccessful.

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Mountain Valley contends that the protesters — some of whom stood within 50 feet of live explosives while smoking cigarettes — were creating a risk to themselves and pipeline workers.

Dillon is being asked to order any observers on the Terry property to “remain a safe distance from blasting on the project.” The motion also asks that Coles and Red Terry be held in contempt of court for violating an earlier order that gave Mountain Valley possession of a 125-foot wide easement through their land under the laws of eminent domain.

Joe Sherman, a Norfolk attorney who represents the Terrys, said Friday that Mountain Valley is seeking an injunction that would give it control of private property beyond the reach of its easement.

“The Court should decline the invitation to read ambiguous power into MVP’s easement documents. MVP must revise its own easement if it needs to restrict the use of private property beyond the pipeline project corridor,” he wrote in an email.

Sherman also voiced concerns about another subpoena, this one issued to Red Terry seeking her social media comments and other information, including a list of visitors to her property.

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Terry said Friday that she doesn’t spend much time on Facebook, and that her only goal was to protect the water on rural property that has been in her family for seven generations.

“They [Mountain Valley] want to create the illusion that I’m out here just raising hell,” said Terry, who in 2018 spent more than a month in a tree stand in effort to prevent tree-cutting on her land.

Appalachians Against Pipelines said in a statement that it will continue to share information about grassroots opposition to the 303-mile pipeline on social media.

“This isn’t the first time that MVP has used intimidation tactics to try to stop resistance to the pipeline, and it won’t be the last,” the statement read.

No date has been scheduled for a hearing on Mountain Valley’s request for an injunction that would keep protesters away from its blasting sites. The subpoenas to Facebook demand that the social media giant produce the requested information by Sept. 17.

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When someone sets up a page on Facebook, they are given the option of whether or not they want to be publicly identified or not. Appalachians Against Pipelines chose the latter, it said.

Usually when a subpoena is issued, Facebook will inform administrators of its pages, Schwartz said. It is then up to the individuals to challenge the subpoena.

One administrator of the Facebook page had not heard anything from the company by Friday, a spokesperson for the group said. Facebook had not responded by Friday evening to two emails sent by The Roanoke Times.

Appalachians Against Pipelines has been a vocal critic of the pipeline, which has come under fierce fire for its use of eminent domain to take private property, failures to control muddy runoff from construction sites, and contribution to climate change.

But its approach is different from other organizations such as the Sierra Club, Appalachian Voices, the Protect Our Water, Heritage, Rights coalition and Wild Virginia, who among others have actively participated in public meetings and filed legal challenges of the permits issued to the pipeline.

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The main sign of Appalachians Against Pipelines has been its presence on social media.

Occasionally, it will issue news releases when there is a protest, such as a recent blockade on Poor Mountain by three people who occupied a junked car parked in the pipeline right of way.

The spokesperson for Appalachians Against Pipelines, who asked to remain anonymous, declined to answer questions about the group’s organizational structure, such as whether it has officers or a formal membership list.

But, the statement said, “This campaign, and the dozens of other struggles against pipelines and fossil fuel infrastructure across the continent, are stronger than MVP and others want people to believe.”

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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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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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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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